Dimension grouping and reduction for model generation, testing, and documentation

ABSTRACT

An example method includes receiving analysis data and output indicator, mapping data points from a transposition of the analysis data to a reference space, generating a cover of the reference space, clustering the data points mapped to the reference space using the cover and a metric function to determine each node of a plurality of nodes, for each node, identifying data points that are members to identify similar features, grouping features as being similar to each other based on node(s), for each feature, determining correlation with at least some data associated with the output indicator and generate a correlation score, displaying at least groupings of similar features and displaying the correlation scores, receiving a selection of features, generating a set of models based on selection, determining fit of each generated model to output data and generate a model score, and generating a model recommendation report.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/418,709 filed Nov. 7, 2016 and entitled“Analytical Modeling, Fitting and Optimized Selection,” which is herebyincorporated by reference herein.

BACKGROUND 1. Field of the Invention

Embodiments of the present invention(s) are directed to grouping of datapoints for data analysis and more particularly to generating a graphutilizing improved groupings of data points based on scores of thegroupings.

2. Related Art

As the collection and storage data has increased, there is an increasedneed to analyze and make sense of large amounts of data. Examples oflarge datasets may be found in financial services companies, oilexpiration, biotech, and academia. Unfortunately, previous methods ofanalysis of large multidimensional datasets tend to be insufficient (ifpossible at all) to identify important relationships and may becomputationally inefficient.

In one example, previous methods of analysis often use clustering.Clustering is often too blunt an instrument to identify importantrelationships in the data. Similarly, previous methods of linearregression, projection pursuit, principal component analysis, andmultidimensional scaling often do not reveal important relationships.Existing linear algebraic and analytic methods are too sensitive tolarge scale distances and, as a result, lose detail.

Further, even if the data is analyzed, sophisticated experts are oftennecessary to interpret and understand the output of previous methods.Although some previous methods allow graphs depicting some relationshipsin the data, the graphs are not interactive and require considerabletime for a team of such experts to understand the relationships.Further, the output of previous methods does not allow for exploratorydata analysis where the analysis can be quickly modified to discover newrelationships. Rather, previous methods require the formulation of ahypothesis before testing.

An increasingly complex world demands a different type of modelingsolution—one that prioritizes scope, collaboration, speed, accuracy,impartiality and transparency. Unfortunately, that is not what permeatesthe data modeling world.

Most modeling tools are structured in a way that the subject matterexpert is effectively excluded from the process of developing the modeluntil after it is built, creating significant risk for the organizationacross a number dimensions. The reason that organizations excludesubject matter experts has to do with how the modeling process works inmost enterprises. Presently, 80% of the modeling timeline is dedicatedto extracting and preparing data (ETL). What this means is that 20% ofthe modeling timeline goes into actually building and validating themodel. Given the scheduling constraints associated with iteration, thisleaves little time to explore the problem from a principled perspective.With no time and minimal business input, enterprises fall back onbuilding an algorithmic black box because it is faster and easier thanthe alternative.

Further, for the vast majority of modeling solutions on the markettoday, the only interface that a business user or regulator has to themodel is PowerPoint or Word. This is particularly problematic for thebusiness users. Those with the domain knowledge to think through themodel, to guide its development and ensure its applicability to thebusiness rarely ever interact with the model until the results arepresented. Often it takes weeks or months to gain consensus using thisapproach of build, explain, evaluate as stakeholders must be scheduledto interact with the candidate model. This is sub-optimal on multiplelevels and introduces risk for the organization that can be dramaticallyreduced or eliminated altogether.

SUMMARY OF THE INVENTION(S)

An example non-transitory computer readable medium may includeexecutable instructions. The instructions may be executable by aprocessor to perform a method. The method may comprise receivinganalysis data and output indicator, the output indicator indicating asubset of data of the analysis data, the analysis data includingmultiple dimensions associated with data points, receiving a lensfunction identifier, a metric function identifier, and a resolutionfunction identifier, mapping data points from a transposition of theanalysis data, to a reference space utilizing a lens function identifiedby the lens function identifier, the transposition of the analysis datatransforming the analysis data such that the features are data points,the mapping of data points being performed by applying the lensfunctions across dimensions for each data point of the transposition ofthe analysis data, generating a cover of the reference space using aresolution function identified by the resolution identifier, clusteringthe data points mapped to the reference space using the cover and ametric function identified by the metric function identifier todetermine each node of a plurality of nodes of a graph, each nodeincluding at least one data point, for each node, identifying datapoints that are members of that node to identify similar features,grouping features that are members of the same node as being similar toeach other, for each feature, determining correlation with at least someof the subset of data of the analysis data and generate a correlationscore, displaying at least a subset of groups that include features thatare similar to each other and display the correlation score for eachdisplayed feature, receiving a selection of a subset of features fromthe at least the subset of groups, generating a set of models, eachmodel including at least one of the selection of the subset of features,determining fit of each generated model to the subset of data of theanalysis data and generate a model score, and generating a reportrecommending the model with the highest score.

In various embodiments, the method further comprise receiving a maximumnumber of features and wherein generating a set of models comprisinggenerating a model for every possible combination of the selection ofthe subset of features.

The method may further comprise receiving model parameters and whereinevery model that is generated is based on the model parameters.

In some embodiments, the method may further comprise receiving scenariodata and applying a selected model from the set of models to thescenario data to generate scenario results.

The method may further comprise documenting the features of the analysisdata, the subset of the groups that include features that are similar toeach other, the correlation score for each feature, the selection of thesubset of the features, the model with the highest score, and thehighest score.

Documenting may further comprise indicating the features that were notselected and correlation scores of each of the features that were notselected. The method may further comprise generating a group score foreach of the subset of groups, the group score being based on thecorrelation score of each of the features that are members of the group,and ordering each of the subset of the groups based on the group score.

In some embodiments, the metric is one of a set of metrics, and, themethod further comprises for each for each metric of a set of metrics:for each point in the analysis data, determining a point in the data setclosest to that particular data point using that particular metric andchange a metric score if that particular data point and the point in thedata set closest to that particular data point share a same or similarshared characteristic, comparing metric scores associated with differentmetrics of the set of metrics, selecting one or more metrics from theset of metrics based at least in part on the metric score to generate asubset of metrics, for each metric of the subset of metrics, evaluatingat least one metric-lens combination by calculating a metric-lens scorebased on entropy of shared characteristics across subspaces of areference map generated by the metric-lens combination, selecting one ormore metric-lens combinations based at least in part on the metric-lensscore to generate a subset of metric-lens combinations, generatingtopological representations using the received data set, eachtopological representation being generated using at least onemetric-lens combination of the subset of metric-lens combinations, eachtopological representation including a plurality of nodes, each of thenodes having one or more data points from the data set as members, atleast two nodes of the plurality of nodes being connected by an edge ifthe at least two nodes share at least one data point from the data setas members, scoring each group within each topological representationbased, at least in part, on entropy, to generate a group score for eachgroup, and scoring each topological representation based on the groupscores of each group of that particular topological representation togenerate a graph score for each topological representation, wherein thesubset of groups that include features that are similar to each other isselected from nodes of the topological representation with the highestscore. Additionally or not in addition with the above in this paragraph,the method may further comprise building a first partition of subsets ofthe analysis data, each subset of the first partition containingelements being exclusive of other subsets of the first partition,computing a first subset score for each subset of the first partitionusing a scoring function based on the subset of the data of the analysisdata, generating a next partition including all of the elements of thefirst partition, the next partition including at least one subset thatincludes the elements of two or more subsets of the first partition,each particular subset of the next partition being related to one ormore subsets of a previously generated partition if that particularsubset shares membership of at least one element with the one or moresubsets of the previously generated partition, computing a second subsetscore for each subset of the next partition using the scoring function,defining a max score for each particular subset of the next partitionusing a max score function, each max score being based on maximal subsetscores of that particular subset of the next partition and at least thesubsets of the first partition related to that particular subset,selecting output subsets from all subsets of the next partition and thepreviously generated partitions including the first partition, theoutput subsets together including all elements of the first partition,selection of each of the output subsets being made, at least in part,using a maximum score of previously computed subset scores, the maximumscore being a largest score of all subset scores of the next partitionand previously generated partitions including the first partition, andwherein the subset of groups that include features that are similar toeach other is selected from nodes of an output partition containing theoutput subsets, the output subsets of the output partition beingassociated with the received analysis data, each subset of the outputpartition containing elements being exclusive of other subsets of theoutput partition.

An example method may comprise receiving analysis data and outputindicator, the output indicator indicating a subset of data of theanalysis data, the analysis data including multiple dimensionsassociated with data points, receiving a lens function identifier, ametric function identifier, and a resolution function identifier,mapping data points from a transposition of the analysis data, to areference space utilizing a lens function identified by the lensfunction identifier, the transposition of the analysis data transformingthe analysis data such that the features are data points, the mapping ofdata points being performed by applying the lens functions acrossdimensions for each data point of the transposition of the analysisdata, generating a cover of the reference space using a resolutionfunction identified by the resolution identifier, clustering the datapoints mapped to the reference space using the cover and a metricfunction identified by the metric function identifier to determine eachnode of a plurality of nodes of a graph, each node including at leastone data point, for each node, identifying data points that are membersof that node to identify similar features, grouping features that aremembers of the same node as being similar to each other, for eachfeature, determining correlation with at least some of the subset ofdata of the analysis data and generate a correlation score, displayingat least a subset of groups that include features that are similar toeach other and display the correlation score for each displayed feature,receiving a selection of a subset of features from the at least thesubset of groups, generating a set of models, each model including atleast one of the selection of the subset of features, determining fit ofeach generated model to the subset of data of the analysis data andgenerate a model score, and generating a report recommending the modelwith the highest score.

An example system comprises a processor and memory includinginstructions to configure the processor to: receive analysis data andoutput indicator, the output indicator indicating a subset of data ofthe analysis data, the analysis data including multiple dimensionsassociated with data points, receive a lens function identifier, ametric function identifier, and a resolution function identifier, mapdata points from a transposition of the analysis data, to a referencespace utilizing a lens function identified by the lens functionidentifier, the transposition of the analysis data transforming theanalysis data such that the features are data points, the mapping ofdata points being performed by applying the lens functions acrossdimensions for each data point of the transposition of the analysisdata, generate a cover of the reference space using a resolutionfunction identified by the resolution identifier, cluster the datapoints mapped to the reference space using the cover and a metricfunction identified by the metric function identifier to determine eachnode of a plurality of nodes of a graph, each node including at leastone data point, for each node, identify data points that are members ofthat node to identify similar features, group features that are membersof the same node as being similar to each other, for each feature,determine correlation with at least some of the subset of data of theanalysis data and generate a correlation score, display at least asubset of groups that include features that are similar to each otherand display the correlation score for each displayed feature, receive aselection of a subset of features from the at least the subset ofgroups, generate a set of models, each model including at least one ofthe selection of the subset of features, determine fit of each generatedmodel to the subset of data of the analysis data and generate a modelscore, and generate a report recommending the model with the highestscore.

Exemplary systems and methods for outcome automatic analysis aredescribed. In various embodiments, a non-transitory computer readablemedium including executable instructions, the instructions beingexecutable by a processor to perform a method. The method may comprisereceiving a data set, for each metric of a set of metrics: for eachpoint in the data set, determining a point in the data set closest tothat particular data point using that particular metric and change ametric score if that particular data point and the point in the data setclosest to that particular data point share a same or similar sharedcharacteristic, comparing metric scores associated with differentmetrics of the set of metrics, selecting one or more metrics from theset of metrics based at least in part on the metric score to generate asubset of metrics, for each metric of the subset of metrics, evaluatingat least one metric-lens combination by calculating a metric-lens scorebased on entropy of shared characteristics across subspaces of areference map generated by the metric-lens combination, selecting one ormore metric-lens combinations based at least in part on the metric-lensscore to generate a subset of metric-lens combinations, generatingtopological representations using the received data set, eachtopological representation being generated using at least onemetric-lens combination of the subset of metric-lens combinations, eachtopological representation including a plurality of nodes, each of thenodes having one or more data points from the data set as members, atleast two nodes of the plurality of nodes being connected by an edge ifthe at least two nodes share at least one data point from the data setas members, associating each node with at least one sharedcharacteristic based, at least in part, on at least some of member datapoints of that particular node sharing the shared characteristic,identifying groups within each topological representation that include asubset of nodes of the plurality of nodes that share the same or similarshared characteristics, scoring each group within each topologicalrepresentation based, at least in part, on entropy, to generate a groupscore for each group, scoring each topological representation based onthe group scores of each group of that particular topologicalrepresentation to generate a graph score for each topologicalrepresentation, and providing a visualization of at least onetopological representation based on the graph scores.

The metric-lens combination may include at least one metric from thesubset of metrics and two or more lenses. The shared characteristic maybe a category of outcome from the received data set. The method mayfurther comprise calculating the entropy of shared characteristicsacross subspaces of a reference map generated by the metric-lenscombination by calculating the entropy of categories of outcomes of datapoints from the data set associated with at least one subspace of thereference map.

In some embodiments, the method may further comprise determining aresolution for generation of one or more topological representation ofthe topological representations, the resolution being determined asfollows:

${res} = \left( {\left\lbrack {\max\left( {\frac{{gain}*N}{L_{n}*100},10} \right)} \right\rbrack^{L_{n}} + \left( {\frac{\sqrt{N}}{4}*j} \right)^{L_{n}}} \right)^{\frac{1}{L_{n}}}$the resolution being determined for each j in [0, number of resolutionsto be considered −1], Ln is a number of metric-lens combinations, and Nis the number of points in the resolution mapping.

The visualization may be interactive. Providing the visualization mayinclude providing at least one of metric information, metric-lensinformation, or graph score. Providing the visualization may includeproviding a plurality of visualizations in order of the graph score foreach of the provided visualizations.

Generating the topological representations using the receive data setmay comprise generating a plurality of reference spaces using eachmetric-lens combination, mapping the data points of the data set intoeach reference space using a different metric-lens combination, and foreach reference space: clustering data in a cover of the reference spacebased the data points of the data set, identifying nodes of theplurality of nodes based on the clustered data, and identifying edgesbetween nodes.

In some embodiments, the topological representation may not be avisualization. In various embodiments, the score for each topologicalrepresentation is calculated as follows:

$\left( {{\sum\limits_{{groups}\mspace{14mu} g}{{{entropy}(g)}*\#{{pts}(g)}}} + \frac{N}{50*\#{{pts}(g)}}} \right)*\left\{ {{{{if}\mspace{14mu}\#\;{groups}} < {\#{cat}}},{{then}\mspace{11mu}\frac{\#{cats}}{\#{groups}}},{{else}\mspace{14mu} 1}} \right.$wherein groups g is each g of a topological representation, entropy (g)is the entropy of that particular group, #pts(g) is the number of datapoints in that particular group, N is the number of nodes in the group,# groups is the number of groups in the particular topologicalrepresentation and # cats is the number of categories of sharedcharacteristics of the data set.

An example method may comprise receiving a data set, for each metric ofa set of metrics: for each point in the data set, determining a point inthe data set closest to that particular data point using that particularmetric and change a metric score if that particular data point and thepoint in the data set closest to that particular data point share a sameor similar shared characteristic, comparing metric scores associatedwith different metrics of the set of metrics, selecting one or moremetrics from the set of metrics based at least in part on the metricscore to generate a subset of metrics, for each metric of the subset ofmetrics, evaluating at least one metric-lens combination by calculatinga metric-lens score based on entropy of shared characteristics acrosssubspaces of a reference map generated by the metric-lens combination,selecting one or more metric-lens combinations based at least in part onthe metric-lens score to generate a subset of metric-lens combinations,generating topological representations using the received data set, eachtopological representation being generated using at least onemetric-lens combination of the subset of metric-lens combinations, eachtopological representation including a plurality of nodes, each of thenodes having one or more data points from the data set as members, atleast two nodes of the plurality of nodes being connected by an edge ifthe at least two nodes share at least one data point from the data setas members, associating each node with at least one sharedcharacteristic based, at least in part, on at least some of member datapoints of that particular node sharing the shared characteristic,identifying groups within each topological representation that include asubset of nodes of the plurality of nodes that share the same or similarshared characteristics, scoring each group within each topologicalrepresentation based, at least in part, on entropy, to generate a groupscore for each group, scoring each topological representation based onthe group scores of each group of that particular topologicalrepresentation to generate a graph score for each topologicalrepresentation, and providing a visualization of at least onetopological representation based on the graph scores.

An example system may comprise a processor and a memory withinstructions to configure the processor to receive a data set, for eachmetric of a set of metrics: for each point in the data set, determine apoint in the data set closest to that particular data point using thatparticular metric and change a metric score if that particular datapoint and the point in the data set closest to that particular datapoint share a same or similar shared characteristic, compare metricscores associated with different metrics of the set of metrics, selectone or more metrics from the set of metrics based at least in part onthe metric score to generate a subset of metrics, for each metric of thesubset of metrics, evaluate at least one metric-lens combination bycalculating a metric-lens score based on entropy of sharedcharacteristics across subspaces of a reference map generated by themetric-lens combination, select one or more metric-lens combinationsbased at least in part on the metric-lens score to generate a subset ofmetric-lens combinations, generate topological representations using thereceived data set, each topological representation being generated usingat least one metric-lens combination of the subset of metric-lenscombinations, each topological representation including a plurality ofnodes, each of the nodes having one or more data points from the dataset as members, at least two nodes of the plurality of nodes beingconnected by an edge if the at least two nodes share at least one datapoint from the data set as members, associate each node with at leastone shared characteristic based, at least in part, on at least some ofmember data points of that particular node sharing the sharedcharacteristic, identify groups within each topological representationthat include a subset of nodes of the plurality of nodes that share thesame or similar shared characteristics, score each group within eachtopological representation based, at least in part, on entropy, togenerate a group score for each group, score each topologicalrepresentation based on the group scores of each group of thatparticular topological representation to generate a graph score for eachtopological representation, and provide a visualization of at least onetopological representation based on the graph scores.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is an example graph representing data that appears to be dividedinto three disconnected groups.

FIG. 1B is an example graph representing data set obtained from aLotka-Volterra equation modeling the populations of predators and preyover time.

FIG. 1C is an example graph of data sets whereby the data does not breakup into disconnected groups, but instead has a structure in which thereare lines (or flares) emanating from a central group.

FIG. 2 is an exemplary environment in which embodiments may bepracticed.

FIG. 3 is a block diagram of an exemplary analysis server.

FIG. 4 is a flow chart depicting an exemplary method of dataset analysisand visualization in some embodiments.

FIG. 5 is an exemplary ID field selection interface window in someembodiments.

FIG. 6A is an exemplary data field selection interface window in someembodiments.

FIG. 6B is an exemplary metric and filter selection interface window insome embodiments.

FIG. 7 is an exemplary filter parameter interface window in someembodiments.

FIG. 8 is a flowchart for data analysis and generating a visualizationin some embodiments.

FIG. 9 is an exemplary interactive visualization in some embodiments.

FIG. 10 is an exemplary interactive visualization displaying an explaininformation window in some embodiments.

FIG. 11 is a flowchart of functionality of the interactive visualizationin some embodiments.

FIG. 12 is a flowchart of for generating a cancer map visualizationutilizing biological data of a plurality of patients in someembodiments.

FIG. 13 is an exemplary data structure including biological data for anumber of patients that may be used to generate the cancer mapvisualization in some embodiments.

FIG. 14 is an exemplary visualization displaying the cancer map in someembodiments.

FIG. 15 is a flowchart of for positioning new patient data relative tothe cancer map visualization in some embodiments.

FIG. 16 is an exemplary visualization displaying the cancer mapincluding positions for three new cancer patients in some embodiments.

FIG. 17 is a flowchart of utilization the visualization and positioningof new patient data in some embodiments.

FIG. 18 is an exemplary digital device in some embodiments.

FIGS. 19A-19D depict an example of determining a partition based onscoring for autogrouping in some embodiments.

FIG. 20 is a block diagram of an exemplary analysis server.

FIG. 21 depicts an example autogroup module in some embodiments.

FIG. 22 is an example flowchart for autogrouping in some embodiments.

FIG. 23 depicts an example of determining a partition based on scoringin some embodiments.

FIG. 24 is an example report of an autogrouped graph of data points thatdepicts the grouped data in some embodiments.

FIG. 25 is an example visualization generated based on an input graph,each edge being weighted by a difference of a density function at theedge endpoints.

FIG. 26 is another example visualization generated using autogroupedpartitions of a graph into regions that are strongly connected and havesimilar function values.

FIG. 27 depicts a visualization of a graph that illustrates outcomesthat are not significantly localized.

FIG. 28 depicts a visualization of a graph that illustrates outcomesthat are more localized than FIG. 27.

FIG. 29 is a block diagram of an exemplary analysis server including anautogroup module and an outcome analysis module.

FIG. 30 depicts an example outcome analysis module in some embodiments.

FIG. 31 is a flowchart for outcome auto analysis in some embodiments.

FIG. 32 is a flowchart for selection of a subset of metrics in someembodiments.

FIG. 33A depicts groupings of data points with fairly consistentoutcomes.

FIG. 33B depicts an example graph using a Manhattan metric. Like FIG.33A, FIG. 33B depicts groupings of data points with fairly consistentoutcomes.

FIG. 33C depicts an example graph using an Absolute Correlation metric.FIG. 33C depicts one large group with different outcomes that areintermixed.

FIG. 34A depicts groupings of data points with fairly consistentoutcomes although there are some relatively minor intermixed data.

FIG. 34B depicts an example graph using a Euclidean metric.

FIG. 34C depicts an example graph using a Norm. Correlation metric.

FIG. 34D depicts an example graph using an Chebyshev metric.

FIG. 35 is a flowchart of selection of a subset of metric-lenscombinations in some embodiments.

FIG. 36A depicts an example graph using a Euclidean (L2) metric with aneighborhood lens.

FIG. 36B depicts an example graph using an angle metric and MDS Coord. 2lens.

FIG. 36C depicts an example graph using a Euclidean (L1) using aneighborhood lens.

FIG. 37A depicts groupings of data points with some mixed outcomes.

FIG. 37B depicts an example graph using a Euclidean metric andneighborhood lens.

FIG. 37C depicts an example graph using a Cosine using a PCA lens.

FIG. 38 is a flowchart for identifying one or more graphs based on agraph score using the metric-lens combinations of the subset ofmetric-lens combinations.

FIG. 39A depicts a visualization of a graph using a Chebyshev(L-Infinity) metric and a neighborhood lens 2 (resolution 61, gain of4.0).

FIG. 39B depicts a visualization of a graph using a variance normalizedEuclidean metric and a neighborhood lens 1 (resolution 57, gain of 3.0).

FIG. 39C depicts a visualization of a graph using a variance normalizedEuclidean metric and a neighborhood lenses 1 and 2 (resolution 30, gainof 2.0).

FIG. 39D depicts a visualization of a graph using a Euclidean (L2)metric and a neighborhood lenses 1 and 2 (resolution 13, gain of 2.0).

FIG. 40A depicts a visualization of a graph using an Angle metric and aneighborhood lens 1 (resolution 414, gain of 4.0).

FIG. 40B depicts a visualization of a graph using a Euclidean (L2)metric and neighborhood lenses 1 and 2 (resolution 84, gain of 3.0).

FIG. 40C depicts a visualization of a graph using a Manhattan (L1)metric and neighborhood lenses 1 and 2 (resolution 92, gain of 3.0).

FIG. 40D depicts a visualization of a graph using a cosine metric and aneighborhood lens 1 (resolution 282, gain of 4.0).

FIG. 41 depicts an example workflow in some embodiments.

FIG. 42 is an exemplary environment in which embodiments may bepracticed.

FIG. 43a-c depicts a data scientist perspective for model developmentusing the modeler system in some embodiments.

FIG. 43d depicts an example process showing how reduction of features tothose that will be most relevant to output (and prediction) may beutilized to reduce a number of relevant models that are generated andevaluated

FIG. 44 depicts an example modeler system in some embodiments.

FIG. 45a-b is a flowchart for feature grouping, reduction, modelgeneration, and model recommendation in some embodiments.

FIG. 46 depicts an example data and model parameter interface for aprobability of default (PD) model.

FIG. 47 depicts an example of transposed, transformed, analysis data.

FIG. 48 depicts an example of scenario data.

FIG. 49 is a flowchart for data analysis in some embodiments.

FIG. 50 is a flowchart for outcome auto analysis to test different lensfunctions and/or different filter functions in some embodiments.

FIG. 51 is an example flowchart for autogrouping in some embodiments.

FIG. 52 depicts a group feature interface for a PD model in someembodiments.

FIG. 53 depicts a group feature interface for a PD model where a subsetof features are selected in some embodiments.

FIG. 54 depicts a regression graph for model fitting using one modelgenerated by the model generation module.

FIG. 55 depicts a select model interface that may be generated by theinterface module in some embodiments.

FIG. 56 depicts an example model report in some embodiments

DETAILED DESCRIPTION OF THE DRAWINGS

Some embodiments described herein may be a part of the subject ofTopological Data Analysis (TDA). TDA is an area of research which hasproduced methods for studying point cloud data sets from a geometricpoint of view. Other data analysis techniques use “approximation bymodels” of various types. For example, regression methods model the dataas the graph of a function in one or more variables. Unfortunately,certain qualitative properties (which one can readily observe when thedata is two-dimensional) may be of a great deal of importance forunderstanding, and these features may not be readily represented withinsuch models.

FIG. 1A is an example graph representing data that appears to be dividedinto three disconnected groups. In this example, the data for this graphmay be associated with various physical characteristics related todifferent population groups or biomedical data related to differentforms of a disease. Seeing that the data breaks into groups in thisfashion can give insight into the data, once one understands whatcharacterizes the groups.

FIG. 1B is an example graph representing data set obtained from aLotka-Volterra equation modeling the populations of predators and preyover time. From FIG. 1B, one observation about this data is that it isarranged in a loop. The loop is not exactly circular, but it istopologically a circle. The exact form of the equations, whileinteresting, may not be of as much importance as this qualitativeobservation which reflects the fact that the underlying phenomenon isrecurrent or periodic. When looking for periodic or recurrent phenomena,methods may be developed which can detect the presence of loops withoutdefining explicit models. For example, periodicity may be detectablewithout having to first develop a fully accurate model of the dynamics.

FIG. 1C is an example graph of data sets whereby the data does not breakup into disconnected groups, but instead has a structure in which thereare lines (or flares) emanating from a central group. In this case, thedata also suggests the presence of three distinct groups, but theconnectedness of the data does not reflect this. This particular datathat is the basis for the example graph in FIG. 1C arises from a studyof single nucleotide polymorphisms (SNPs).

In each of the examples above, aspects of the shape of the data arerelevant in reflecting information about the data. Connectedness (thesimplest property of shape) reflects the presence of a discreteclassification of the data into disparate groups. The presence of loops,another simple aspect of shape, often reflect periodic or recurrentbehavior. Finally, in the third example, the shape containing flaressuggests a classification of the data descriptive of ways in whichphenomena can deviate from the norm, which would typically berepresented by the central core. These examples support the idea thatthe shape of data (suitably defined) is an important aspect of itsstructure, and that it is therefore important to develop methods foranalyzing and understanding its shape. The part of mathematics whichconcerns itself with the study of shape is called topology, andtopological data analysis attempts to adapt methods for studying shapewhich have been developed in pure mathematics to the study of the shapeof data, suitably defined.

One question is how notions of geometry or shape are translated intoinformation about point clouds, which are, after all, finite sets? Whatwe mean by shape or geometry can come from a dissimilarity function ormetric (e.g., a non-negative, symmetric, real-valued function d on theset of pairs of points in the data set which may also satisfy thetriangle inequality, and d(x; y)=0 if and only if x=y). Such functionsexist in profusion for many data sets. For example, when the data comesin the form of a numerical matrix, where the rows correspond to the datapoints and the columns are the fields describing the data, then-dimensional Euclidean distance function is natural when there are nfields. Similarly, in this example, there are Pearson correlationdistances, cosine distances, and other choices.

When the data is not Euclidean, for example if one is consideringgenomic sequences, various notions of distance may be defined usingmeasures of similarity based on Basic Local Alignment Search Tool(BLAST) type similarity scores. Further, a measure of similarity cancome in non-numeric forms, such as social networks of friends orsimilarities of hobbies, buying patterns, tweeting, and/or professionalinterests. In any of these ways the notion of shape may be formulatedvia the establishment of a useful notion of similarity of data points.

One of the advantages of TDA is that it may depend on nothing more thansuch a notion, which is a very primitive or low-level model. It may relyon many fewer assumptions than standard linear or algebraic models, forexample. Further, the methodology may provide new ways of visualizingand compressing data sets, which facilitate understanding and monitoringdata. The methodology may enable study of interrelationships amongdisparate data sets and/or multiscale/multiresolution study of datasets. Moreover, the methodology may enable interactivity in the analysisof data, using point and click methods.

TDA may be a very useful complement to more traditional methods, such asPrincipal Component Analysis (PCA), multidimensional scaling, andhierarchical clustering. These existing methods are often quite useful,but suffer from significant limitations. PCA, for example, is anessentially linear procedure and there are therefore limits to itsutility in highly non-linear situations. Multidimensional scaling is amethod which is not intrinsically linear, but can in many situationswash out detail, since it may overweight large distances. In addition,when metrics do not satisfy an intrinsic flatness condition, it may havedifficulty in faithfully representing the data. Hierarchical clusteringdoes exhibit multiscale behavior, but represents data only as disjointclusters, rather than retaining any of the geometry of the data set. Inall four cases, these limitations matter for many varied kinds of data.

We now summarize example properties of an example construction, in someembodiments, which may be used for representing the shape of data setsin a useful, understandable fashion as a finite graph:

-   -   The input may be a collection of data points equipped in some        way with a distance or dissimilarity function, or other        description. This can be given implicitly when the data is in        the form of a matrix, or explicitly as a matrix of distances or        even the generating edges of a mathematical network.    -   One construction may also use one or more lens functions (i.e.        real valued functions on the data). Lens function(s) may depend        directly on the metric. For example, lens function(s) might be        the result of a density estimator or a measure of centrality or        data depth. Lens function(s) may, in some embodiments, depend on        a particular representation of the data, as when one uses the        first one or two coordinates of a principal component or        multidimensional scaling analysis. In some embodiments, the lens        function(s) may be columns which expert knowledge identifies as        being intrinsically interesting, as in cholesterol levels and        BMI in a study of heart disease.    -   In some embodiments, the construction may depend on a choice of        two or more processing parameters, resolution, and gain.        Increase in resolution typically results in more nodes and an        increase in the gain increases the number of edges in a        visualization and/or graph in a reference space as further        described herein.    -   The output may be, for example, a visualization (e.g., a display        of connected nodes or “network”) or simplicial complex. One        specific combinatorial formulation in one embodiment may be that        the vertices form a finite set, and then the additional        structure may be a collection of edges (unordered pairs of        vertices) which are pictured as connections in this network.

In various embodiments, a system for handling, analyzing, andvisualizing data using drag and drop methods as opposed to text basedmethods is described herein. Philosophically, data analytic tools arenot necessarily regarded as “solvers,” but rather as tools forinteracting with data. For example, data analysis may consist of severaliterations of a process in which computational tools point to regions ofinterest in a data set. The data set may then be examined by people withdomain expertise concerning the data, and the data set may then besubjected to further computational analysis. In some embodiments,methods described herein provide for going back and forth betweenmathematical constructs, including interactive visualizations (e.g.,graphs), on the one hand and data on the other.

In one example of data analysis in some embodiments described herein, anexemplary clustering tool is discussed which may be more powerful thanexisting technology, in that one can find structure within clusters andstudy how clusters change over a period of time or over a change ofscale or resolution.

An exemplary interactive visualization tool (e.g., a visualizationmodule which is further described herein) may produce combinatorialoutput in the form of a graph which can be readily visualized. In someembodiments, the exemplary interactive visualization tool may be lesssensitive to changes in notions of distance than current methods, suchas multidimensional scaling.

Some embodiments described herein permit manipulation of the data from avisualization. For example, portions of the data which are deemed to beinteresting from the visualization can be selected and converted intodatabase objects, which can then be further analyzed. Some embodimentsdescribed herein permit the location of data points of interest withinthe visualization, so that the connection between a given visualizationand the information the visualization represents may be readilyunderstood.

FIG. 2 is an exemplary environment 200 in which embodiments may bepracticed. In various embodiments, data analysis and interactivevisualization may be performed locally (e.g., with software and/orhardware on a local digital device), across a network (e.g., via cloudcomputing), or a combination of both. In many of these embodiments, adata structure is accessed to obtain the data for the analysis, theanalysis is performed based on properties and parameters selected by auser, and an interactive visualization is generated and displayed. Thereare many advantages between performing all or some activities locallyand many advantages of performing all or some activities over a network.

Environment 200 comprises user devices 202 a-202 n, a communicationnetwork 204, data storage server 206, and analysis server 208.Environment 200 depicts an embodiment wherein functions are performedacross a network. In this example, the user(s) may take advantage ofcloud computing by storing data in a data storage server 206 over acommunication network 204. The analysis server 208 may perform analysisand generation of an interactive visualization.

User devices 202 a-202 n may be any digital devices. A digital device isany device that comprises memory and a processor. Digital devices arefurther described in FIG. 2. The user devices 202 a-202 n may be anykind of digital device that may be used to access, analyze and/or viewdata including, but not limited to a desktop computer, laptop, notebook,or other computing device.

In various embodiments, a user, such as a data analyst, may generate adatabase or other data structure with the user device 202 a to be savedto the data storage server 206. The user device 202 a may communicatewith the analysis server 208 via the communication network 204 toperform analysis, examination, and visualization of data within thedatabase.

The user device 202 a may comprise a client program for interacting withone or more applications on the analysis server 208. In otherembodiments, the user device 202 a may communicate with the analysisserver 208 using a browser or other standard program. In variousembodiments, the user device 202 a communicates with the analysis server208 via a virtual private network. It will be appreciated that thatcommunication between the user device 202 a, the data storage server206, and/or the analysis server 208 may be encrypted or otherwisesecured.

The communication network 204 may be any network that allows digitaldevices to communicate. The communication network 204 may be theInternet and/or include LAN and WANs. The communication network 204 maysupport wireless and/or wired communication.

The data storage server 206 is a digital device that is configured tostore data. In various embodiments, the data storage server 206 storesdatabases and/or other data structures. The data storage server 206 maybe a single server or a combination of servers. In one example the datastorage server 206 may be a secure server wherein a user may store dataover a secured connection (e.g., via https). The data may be encryptedand backed-up. In some embodiments, the data storage server 206 isoperated by a third-party such as Amazon's S3 service.

The database or other data structure may comprise large high-dimensionaldatasets. These datasets are traditionally very difficult to analyzeand, as a result, relationships within the data may not be identifiableusing previous methods. Further, previous methods may be computationallyinefficient.

The analysis server 208 is a digital device that may be configured toanalyze data. In various embodiments, the analysis server may performmany functions to interpret, examine, analyze, and display data and/orrelationships within data. In some embodiments, the analysis server 208performs, at least in part, topological analysis of large datasetsapplying metrics, filters, and resolution parameters chosen by the user.The analysis is further discussed in FIG. 8 herein.

The analysis server 208 may generate an interactive visualization of theoutput of the analysis. The interactive visualization allows the user toobserve and explore relationships in the data. In various embodiments,the interactive visualization allows the user to select nodes comprisingdata that has been clustered. The user may then access the underlyingdata, perform further analysis (e.g., statistical analysis) on theunderlying data, and manually reorient the graph(s) (e.g., structures ofnodes and edges described herein) within the interactive visualization.The analysis server 208 may also allow for the user to interact with thedata, see the graphic result. The interactive visualization is furtherdiscussed in FIGS. 9-11.

In some embodiments, the analysis server 208 interacts with the userdevice(s) 202 a-202 n over a private and/or secure communicationnetwork. The user device 202 a may comprise a client program that allowsthe user to interact with the data storage server 206, the analysisserver 208, another user device (e.g., user device 202 n), a database,and/or an analysis application executed on the analysis server 208.

Those skilled in the art will appreciate that all or part of the dataanalysis may occur at the user device 202 a. Further, all or part of theinteraction with the visualization (e.g., graphic) may be performed onthe user device 202 a.

Although two user devices 202 a and 202 n are depicted, those skilled inthe art will appreciate that there may be any number of user devices inany location (e.g., remote from each other). Similarly, there may be anynumber of communication networks, data storage servers, and analysisservers.

Cloud computing may allow for greater access to large datasets (e.g.,via a commercial storage service) over a faster connection. Further, itwill be appreciated that services and computing resources offered to theuser(s) may be scalable.

FIG. 3 is a block diagram of an exemplary analysis server 208. Inexemplary embodiments, the analysis server 208 comprises a processor302, input/output (I/O) interface 304, a communication network interface306, a memory system 308, a storage system 310, and a processing module312. The processor 302 may comprise any processor or combination ofprocessors with one or more cores.

The input/output (I/O) interface 304 may comprise interfaces for variousI/O devices such as, for example, a keyboard, mouse, and display device.The exemplary communication network interface 306 is configured to allowthe analysis server 208 to communication with the communication network204 (see FIG. 2). The communication network interface 306 may supportcommunication over an Ethernet connection, a serial connection, aparallel connection, and/or an ATA connection. The communication networkinterface 306 may also support wireless communication (e.g.,802.11a/b/g/n, WiMax, LTE, WiFi). It will be apparent to those skilledin the art that the communication network interface 306 can support manywired and wireless standards.

The memory system 308 may be any kind of memory including RAM, ROM, orflash, cache, virtual memory, etc. In various embodiments, working datais stored within the memory system 308. The data within the memorysystem 308 may be cleared or ultimately transferred to the storagesystem 310.

The storage system 310 includes any storage configured to retrieve andstore data. Some examples of the storage system 310 include flashdrives, hard drives, optical drives, and/or magnetic tape. Each of thememory system 308 and the storage system 310 comprises acomputer-readable medium, which stores instructions (e.g., softwareprograms) executable by processor 302.

The storage system 310 comprises a plurality of modules utilized byembodiments of discussed herein. A module may be hardware, software(e.g., including instructions executable by a processor), or acombination of both. In one embodiment, the storage system 310 comprisesa processing module 312 which comprises an input module 314, a filtermodule 316, a resolution module 318, an analysis module 320, avisualization engine 322, and database storage 324. Alternativeembodiments of the analysis server 208 and/or the storage system 310 maycomprise more, less, or functionally equivalent components and modules.

The input module 314 may be configured to receive commands andpreferences from the user device 202 a. In various examples, the inputmodule 314 receives selections from the user which will be used toperform the analysis. The output of the analysis may be an interactivevisualization.

The input module 314 may provide the user a variety of interface windowsallowing the user to select and access a database, choose fieldsassociated with the database, choose a metric, choose one or morefilters, and identify resolution parameters for the analysis. In oneexample, the input module 314 receives a database identifier andaccesses a large multi-dimensional database. The input module 314 mayscan the database and provide the user with an interface window allowingthe user to identify an ID field. An ID field is an identifier for eachdata point. In one example, the identifier is unique. The same columnname may be present in the table from which filters are selected. Afterthe ID field is selected, the input module 314 may then provide the userwith another interface window to allow the user to choose one or moredata fields from a table of the database.

Although interactive windows may be described herein, it will beappreciated that any window, graphical user interface, and/or commandline may be used to receive or prompt a user or user device 202 a forinformation.

The filter module 316 may subsequently provide the user with aninterface window to allow the user to select a metric to be used inanalysis of the data within the chosen data fields. The filter module316 may also allow the user to select and/or define one or more filters.

The resolution module 218 may allow the user to select a resolution,including filter parameters. In one example, the user enters a number ofintervals and a percentage overlap for a filter.

The analysis module 320 may perform data analysis based on the databaseand the information provided by the user. In various embodiments, theanalysis module 320 performs an algebraic topological analysis toidentify structures and relationships within data and clusters of data.It will be appreciated that the analysis module 320 may use parallelalgorithms or use generalizations of various statistical techniques(e.g., generalizing the bootstrap to zig-zag methods) to increase thesize of data sets that can be processed. The analysis is furtherdiscussed in FIG. 8. It will be appreciated that the analysis module 320is not limited to algebraic topological analysis but may perform anyanalysis.

The visualization engine 322 generates an interactive visualizationincluding the output from the analysis module 320. The interactivevisualization allows the user to see all or part of the analysisgraphically. The interactive visualization also allows the user tointeract with the visualization. For example, the user may selectportions of a graph from within the visualization to see and/or interactwith the underlying data and/or underlying analysis. The user may thenchange the parameters of the analysis (e.g., change the metric,filter(s), or resolution(s)) which allows the user to visually identifyrelationships in the data that may be otherwise undetectable using priormeans. The interactive visualization is further described in FIGS. 9-11.

The database storage 324 is configured to store all or part of thedatabase that is being accessed. In some embodiments, the databasestorage 324 may store saved portions of the database. Further, thedatabase storage 324 may be used to store user preferences, parameters,and analysis output thereby allowing the user to perform many differentfunctions on the database without losing previous work.

It will be appreciated that that all or part of the processing module312 may be at the user device 202 a or the database storage server 206.In some embodiments, all or some of the functionality of the processingmodule 312 may be performed by the user device 202 a.

In various embodiments, systems and methods discussed herein may beimplemented with one or more digital devices. In some examples, someembodiments discussed herein may be implemented by a computer program(instructions) executed by a processor. The computer program may providea graphical user interface. Although such a computer program isdiscussed, it will be appreciated that embodiments may be performedusing any of the following, either alone or in combination, including,but not limited to, a computer program, multiple computer programs,firmware, and/or hardware.

A module and/or engine may include any processor or combination ofprocessors. In some examples, a module and/or engine may include or be apart of a processor, digital signal processor (DSP), applicationspecific integrated circuit (ASIC), an integrated circuit, and/or thelike. In various embodiments, the module and/or engine may be softwareor firmware.

FIG. 4 is a flow chart 400 depicting an exemplary method of datasetanalysis and visualization in some embodiments. In step 402, the inputmodule 314 accesses a database. The database may be any data structurecontaining data (e.g., a very large dataset of multidimensional data).In some embodiments, the database may be a relational database. In someexamples, the relational database may be used with MySQL, Oracle,Micosoft SQL Server, Aster nCluster, Teradata, and/or Vertica. It willbe appreciated that the database may not be a relational database.

In some embodiments, the input module 314 receives a database identifierand a location of the database (e.g., the data storage server 206) fromthe user device 202 a (see FIG. 2). The input module 314 may then accessthe identified database. In various embodiments, the input module 314may read data from many different sources, including, but not limited toMS Excel files, text files (e.g., delimited or CSV), Matlab .mat format,or any other file.

In some embodiments, the input module 314 receives an IP address orhostname of a server hosting the database, a username, password, and thedatabase identifier. This information (herein referred to as “connectioninformation”) may be cached for later use. It will be appreciated thatthe database may be locally accessed and that all, some, or none of theconnection information may be required. In one example, the user device202 a may have full access to the database stored locally on the userdevice 202 a so the IP address is unnecessary. In another example, theuser device 202 a may already have loaded the database and the inputmodule 314 merely begins by accessing the loaded database.

In various embodiments, the identified database stores data withintables. A table may have a “column specification” which stores the namesof the columns and their data types. A “row” in a table, may be a tuplewith one entry for each column of the correct type. In one example, atable to store employee records might have a column specification suchas:

-   -   employee_id primary key int (this may store the employee's ID as        an integer, and uniquely identifies a row)    -   age int    -   gender char(1) (gender of the employee may be a single character        either M or F)    -   salary double (salary of an employee may be a floating point        number)    -   name varchar (name of the employee may be a variable-length        string)        In this example, each employee corresponds to a row in this        table. Further, the tables in this exemplary relational database        are organized into logical units called databases. An analogy to        file systems is that databases can be thought of as folders and        files as tables. Access to databases may be controlled by the        database administrator by assigning a username/password pair to        authenticate users.

Once the database is accessed, the input module 314 may allow the userto access a previously stored analysis or to begin a new analysis. Ifthe user begins a new analysis, the input module 314 may provide theuser device 202 a with an interface window allowing the user to identifya table from within the database. In one example, the input module 314provides a list of available tables from the identified database.

In step 404, the input module 314 receives a table identifieridentifying a table from within the database. The input module 314 maythen provide the user with a list of available ID fields from the tableidentifier. In step 406, the input module 314 receives the ID fieldidentifier from the user and/or user device 202 a. The ID field is, insome embodiments, the primary key.

Having selected the primary key, the input module 314 may generate a newinterface window to allow the user to select data fields for analysis.In step 408, the input module 314 receives data field identifiers fromthe user device 202 a. The data within the data fields may be lateranalyzed by the analysis module 320.

In step 410, the filter module 316 identifies a metric. In someembodiments, the filter module 316 and/or the input module 314 generatesan interface window allowing the user of the user device 202 a optionsfor a variety of different metrics and filter preferences. The interfacewindow may be a drop down menu identifying a variety of distance metricsto be used in the analysis. Metric options may include, but are notlimited to, Euclidean, DB Metric, variance normalized Euclidean, andtotal normalized Euclidean. The metric and the analysis are furtherdescribed herein.

In step 412, the filter module 316 selects one or more filters. In someembodiments, the user selects and provides filter identifier(s) to thefilter module 316. The role of the filters in the analysis is alsofurther described herein. The filters, for example, may be user defined,geometric, or based on data which has been pre-processed. In someembodiments, the data based filters are numerical arrays which canassign a set of real numbers to each row in the table or each point inthe data generally.

A variety of geometric filters may be available for the user to choose.Geometric filters may include, but are not limited to:

-   -   Density    -   L1 Eccentricity    -   L-infinity Eccentricity    -   Witness based Density    -   Witness based Eccentricity    -   Eccentricity as distance from a fixed point    -   Approximate Kurtosis of the Eccentricity

In step 414, the resolution module 218 defines the resolution to be usedwith a filter in the analysis. The resolution may comprise a number ofintervals and an overlap parameter. In various embodiments, theresolution module 218 allows the user to adjust the number of intervalsand overlap parameter (e.g., percentage overlap) for one or morefilters.

In step 416, the analysis module 320 processes data of selected fieldsbased on the metric, filter(s), and resolution(s) to generate thevisualization. This process is discussed in FIG. 8.

In step 418, the visualization module 322 displays the interactivevisualization. In various embodiments, the visualization may be renderedin two or three dimensional space. The visualization module 322 may usean optimization algorithm for an objective function which is correlatedwith good visualization (e.g., the energy of the embedding). Thevisualization may show a collection of nodes corresponding to each ofthe partial clusters in the analysis output and edges connecting them asspecified by the output. The interactive visualization is furtherdiscussed in FIGS. 9-11.

Although many examples discuss the input module 314 as providinginterface windows, it will be appreciated that all or some of theinterface may be provided by a client on the user device 202 a. Further,in some embodiments, the user device 202 a may be running all or some ofthe processing module 212.

FIGS. 5-7 depict various interface windows to allow the user to makeselections, enter information (e.g., fields, metrics, and filters),provide parameters (e.g., resolution), and provide data (e.g., identifythe database) to be used with analysis. It will be appreciated that anygraphical user interface or command line may be used to make selections,enter information, provide parameters, and provide data.

FIG. 5 is an exemplary ID field selection interface window 500 in someembodiments. The ID field selection interface window 500 allows the userto identify an ID field. The ID field selection interface window 500comprises a table search field 502, a table list 504, and a fieldsselection window 506.

In various embodiments, the input module 314 identifies and accesses adatabase from the database storage 324, user device 202 a, or the datastorage server 206. The input module 314 may then generate the ID fieldselection interface window 500 and provide a list of available tables ofthe selected database in the table list 504. The user may click on atable or search for a table by entering a search query (e.g., a keyword)in the table search field 502. Once a table is identified (e.g., clickedon by the user), the fields selection window 506 may provide a list ofavailable fields in the selected table. The user may then choose a fieldfrom the fields selection window 506 to be the ID field. In someembodiments, any number of fields may be chosen to be the ID field(s).

FIG. 6A is an exemplary data field selection interface window 600 a insome embodiments. The data field selection interface window 600 a allowsthe user to identify data fields. The data field selection interfacewindow 600 a comprises a table search field 502, a table list 504, afields selection window 602, and a selected window 604.

In various embodiments, after selection of the ID field, the inputmodule 314 provides a list of available tables of the selected databasein the table list 504. The user may click on a table or search for atable by entering a search query (e.g., a keyword) in the table searchfield 502. Once a table is identified (e.g., clicked on by the user),the fields selection window 506 may provide a list of available fieldsin the selected table. The user may then choose any number of fieldsfrom the fields selection window 602 to be data fields. The selecteddata fields may appear in the selected window 604. The user may alsodeselect fields that appear in the selected window 604.

It will be appreciated that the table selected by the user in the tablelist 504 may be the same table selected with regard to FIG. 5. In someembodiments, however, the user may select a different table. Further,the user may, in various embodiments, select fields from a variety ofdifferent tables.

FIG. 6B is an exemplary metric and filter selection interface window 600b in some embodiments. The metric and filter selection interface window600 b allows the user to identify a metric, add filter(s), and adjustfilter parameters. The metric and filter selection interface window 600b comprises a metric pull down menu 606, an add filter from databasebutton 608, and an add geometric filter button 610.

In various embodiments, the user may click on the metric pull down menu606 to view a variety of metric options. Various metric options aredescribed herein. In some embodiments, the user may define a metric. Theuser defined metric may then be used with the analysis.

In one example, finite metric space data may be constructed from a datarepository (i.e., database, spreadsheet, or Matlab file). This may meanselecting a collection of fields whose entries will specify the metricusing the standard Euclidean metric for these fields, when they arefloating point or integer variables. Other notions of distance, such asgraph distance between collections of points, may be supported.

The analysis module 320 may perform analysis using the metric as a partof a distance function. The distance function can be expressed by aformula, a distance matrix, or other routine which computes it. The usermay add a filter from a database by clicking on the add filter fromdatabase button 608. The metric space may arise from a relationaldatabase, a Matlab file, an Excel spreadsheet, or other methods forstoring and manipulating data. The metric and filter selection interfacewindow 600 b may allow the user to browse for other filters to use inthe analysis. The analysis and metric function are further described inFIG. 8.

The user may also add a geometric filter 610 by clicking on the addgeometric filter button 610. In various embodiments, the metric andfilter selection interface window 600 b may provide a list of geometricfilters from which the user may choose.

FIG. 7 is an exemplary filter parameter interface window 700 in someembodiments. The filter parameter interface window 700 allows the userto determine a resolution for one or more selected filters (e.g.,filters selected in the metric and filter selection interface window600). The filter parameter interface window 700 comprises a filter namemenu 702, an interval field 704, an overlap bar 706, and a done button708.

The filter parameter interface window 700 allows the user to select afilter from the filter name menu 702. In some embodiments, the filtername menu 702 is a drop down box indicating all filters selected by theuser in the metric and filter selection interface window 600. Once afilter is chosen, the name of the filter may appear in the filter namemenu 702. The user may then change the intervals and overlap for one,some, or all selected filters.

The interval field 704 allows the user to define a number of intervalsfor the filter identified in the filter name menu 702. The user mayenter a number of intervals or scroll up or down to get to a desirednumber of intervals. Any number of intervals may be selected by theuser. The function of the intervals is further discussed in FIG. 8.

The overlap bar 706 allows the user to define the degree of overlap ofthe intervals for the filter identified in the filter name menu 702. Inone example, the overlap bar 706 includes a slider that allows the userto define the percentage overlap for the interval to be used with theidentified filter. Any percentage overlap may be set by the user.

Once the intervals and overlap are defined for the desired filters, theuser may click the done button. The user may then go back to the metricand filter selection interface window 600 and see a new option to runthe analysis. In some embodiments, the option to run the analysis may beavailable in the filter parameter interface window 700. Once theanalysis is complete, the result may appear in an interactivevisualization which is further described in FIGS. 9-11.

It will be appreciated that that interface windows in FIGS. 4-7 areexemplary. The exemplary interface windows are not limited to thefunctional objects (e.g., buttons, pull down menus, scroll fields, andsearch fields) shown. Any number of different functional objects may beused. Further, as described herein, any other interface, command line,or graphical user interface may be used.

FIG. 8 is a flowchart 800 for data analysis and generating aninteractive visualization in some embodiments. In various embodiments,the processing on data and user-specified options is motivated bytechniques from topology and, in some embodiments, algebraic topology.These techniques may be robust and general. In one example, thesetechniques apply to almost any kind of data for which some qualitativeidea of “closeness” or “similarity” exists. The techniques discussedherein may be robust because the results may be relatively insensitiveto noise in the data, user options, and even to errors in the specificdetails of the qualitative measure of similarity, which, in someembodiments, may be generally refer to as “the distance function” or“metric.” It will be appreciated that while the description of thealgorithms below may seem general, the implementation of techniquesdescribed herein may apply to any level of generality.

In step 802, the input module 314 receives data S. In one example, auser identifies a data structure and then identifies ID and data fields.Data S may be based on the information within the ID and data fields. Invarious embodiments, data S is treated as being processed as a finite“similarity space,” where data S has a real-valued function d defined onpairs of points s and t in S, such that:d(s,s)=0d(s,t)=d(t,s)d(s,t)>=0

-   -   These conditions may be similar to requirements for a finite        metric space, but the conditions may be weaker. In various        examples, the function is a metric.

It will be appreciated that data S may be a finite metric space, or ageneralization thereof, such as a graph or weighted graph. In someembodiments, data S be specified by a formula, an algorithm, or by adistance matrix which specifies explicitly every pairwise distance.

In step 804, the input module 314 generates reference space R. In oneexample, reference space R may be a well-known metric space (e.g., suchas the real line). The reference space R may be defined by the user. Instep 806, the analysis module 320 generates a map ref( ) from S into R.The map ref( ) from S into R may be called the “reference map.”

In one example, a reference of map from S is to a reference metric spaceR. R may be Euclidean space of some dimension, but it may also be thecircle, torus, a tree, or other metric space. The map can be describedby one or more filters (i.e., real valued functions on S). These filterscan be defined by geometric invariants, such as the output of a densityestimator, a notion of data depth, or functions specified by the originof S as arising from a data set.

In step 808, the resolution module 218 generates a cover of R based onthe resolution received from the user (e.g., filter(s), intervals, andoverlap—see FIG. 7). The cover of R may be a finite collection of opensets (in the metric of R) such that every point in R lies in at leastone of these sets. In various examples, R is k-dimensional Euclideanspace, where k is the number of filter functions. More precisely in thisexample, R is a box in k-dimensional Euclidean space given by theproduct of the intervals [min_k, max_k], where min_k is the minimumvalue of the k-th filter function on S, and max_k is the maximum value.

For example, suppose there are 2 filter functions, F1 and F2, and thatF1's values range from −1 to +1, and F2's values range from 0 to 5. Thenthe reference space is the rectangle in the x/y plane with corners(−1,0), (1,0), (−1, 5), (1, 5), as every point s of S will give rise toa pair (F1(s), F2(s)) that lies within that rectangle.

In various embodiments, the cover of R is given by taking products ofintervals of the covers of [min_k,max_k] for each of the k filters. Inone example, if the user requests 2 intervals and a 50% overlap for F1,the cover of the interval [−1,+1] will be the two intervals (−1.5, 0.5),(−0.5, 1.5). If the user requests 5 intervals and a 30% overlap for F2,then that cover of [0, 5] will be (−0.3, 1.3), (0.7, 2.3), (1.7, 3.3),(2.7, 4.3), (3.7, 5.3). These intervals may give rise to a cover of the2-dimensional box by taking all possible pairs of intervals where thefirst of the pair is chosen from the cover for F1 and the second fromthe cover for F2. This may give rise to 2*5, or 10, open boxes thatcovered the 2-dimensional reference space. However, it will beappreciated that the intervals may not be uniform, or that the covers ofa k-dimensional box may not be constructed by products of intervals. Insome embodiments, there are many other choices of intervals. Further, invarious embodiments, a wide range of covers and/or more generalreference spaces may be used.

In one example, given a cover, C1, . . . , Cm, of R, the reference mapis used to assign a set of indices to each point in S, which are theindices of the Cj such that ref(s) belongs to Cj. This function may becalled ref_tags(s). In a language such as Java, ref_tags would be amethod that returned an int[ ]. Since the C's cover R in this example,ref(s) must lie in at least one of them, but the elements of the coverusually overlap one another, which means that points that “land near theedges” may well reside in multiple cover sets. In considering the twofilter example, if F1(s) is −0.99, and F2(s) is 0.001, then ref(s) is(−0.99, 0.001), and this lies in the cover element (−1.5,0.5)×(−0.3,1.3). Supposing that was labeled C1, the reference map mayassign s to the set {1}. On the other hand, if t is mapped by F1, F2 to(0.1, 2.1), then ref(t) will be in (−1.5,0.5)×(0.7, 2.3), (−0.5,1.5)×(0.7,2.3), (−1.5,0.5)×(1.7,3.3), and (−0.5, 1.5)×(1.7,3.3), so theset of indices would have four elements for t.

Having computed, for each point, which “cover tags” it is assigned to,for each cover element, Cd, the points may be constructed, whose tagsinclude d, as set S(d). This may mean that every point s is in S(d) forsome d, but some points may belong to more than one such set. In someembodiments, there is, however, no requirement that each S(d) isnon-empty, and it is frequently the case that some of these sets areempty. In the non-parallelized version of some embodiments, each point xis processed in turn, and x is inserted into a hash-bucket for each j inref_tags(t) (that is, this may be how S(d) sets are computed).

It will be appreciated that the cover of the reference space R may becontrolled by the number of intervals and the overlap identified in theresolution (e.g., see FIG. 7). For example, the more intervals, thefiner the resolution in S—that is, the fewer points in each S(d), butthe more similar (with respect to the filters) these points may be. Thegreater the overlap, the more times that clusters in S(d) may intersectclusters in S(e)—this means that more “relationships” between points mayappear, but, in some embodiments, the greater the overlap, the morelikely that accidental relationships may appear.

In step 810, the analysis module 320 clusters each S(d) based on themetric, filter, and the space S. In some embodiments, a dynamicsingle-linkage clustering algorithm may be used to partition S(d). Itwill be appreciated that any number of clustering algorithms may be usedwith embodiments discussed herein. For example, the clustering schememay be k-means clustering for some k, single linkage clustering, averagelinkage clustering, or any method specified by the user.

The significance of the user-specified inputs may now be seen. In someembodiments, a filter may amount to a “forced stretching” in a certaindirection. In some embodiments, the analysis module 320 may not clustertwo points unless ALL of the filter values are sufficiently “related”(recall that while normally related may mean “close,” the cover mayimpose a much more general relationship on the filter values, such asrelating two points s and t if ref(s) and ref(t) are sufficiently closeto the same circle in the plane). In various embodiments, the ability ofa user to impose one or more “critical measures” makes this techniquemore powerful than regular clustering, and the fact that these filterscan be anything, is what makes it so general.

The output may be a simplicial complex, from which one can extract its1-skeleton. The nodes of the complex may be partial clusters, (i.e.,clusters constructed from subsets of S specified as the preimages ofsets in the given covering of the reference space R).

In step 812, the visualization engine 322 identifies nodes which areassociated with a subset of the partition elements of all of the S(d)for generating an interactive visualization. For example, suppose thatS={1, 2, 3, 4}, and the cover is C1, C2, C3. Then if ref_tags(1)={1, 2,3} and ref_tags(2)={2, 3}, and ref_tags(3)={3}, and finallyref_tags(4)={1, 3}, then S(1) in this example is {1, 4}, S(2)={1,2}, andS(3)={1,2,3,4}. If 1 and 2 are close enough to be clustered, and 3 and 4are, but nothing else, then the clustering for S(1) may be 11 {3}, andfor S(2) it may be {1,2}, and for S(3) it may be {1,2}, {3,4}. So thegenerated graph has, in this example, at most four nodes, given by thesets {1}, {4}, {1,2}, and {3,4} (note that {1,2} appears in twodifferent clusterings). Of the sets of points that are used, two nodesintersect provided that the associated node sets have a non-emptyintersection (although this could easily be modified to allow users torequire that the intersection is “large enough” either in absolute orrelative terms).

Nodes may be eliminated for any number of reasons. For example, a nodemay be eliminated as having too few points and/or not being connected toanything else. In some embodiments, the criteria for the elimination ofnodes (if any) may be under user control or have application-specificrequirements imposed on it. For example, if the points are consumers,for instance, clusters with too few people in area codes served by acompany could be eliminated. If a cluster was found with “enough”customers, however, this might indicate that expansion into area codesof the other consumers in the cluster could be warranted.

In step 814, the visualization engine 322 joins clusters to identifyedges (e.g., connecting lines between nodes). Once the nodes areconstructed, the intersections (e.g., edges) may be computed “all atonce,” by computing, for each point, the set of node sets (not ref_tags,this time). That is, for each s in S, node_id_set(s) may be computed,which is an int[ ]. In some embodiments, if the cover is well behaved,then this operation is linear in the size of the set S, and we theniterate over each pair in node_id_set(s). There may be an edge betweentwo node_id's if they both belong to the same node_id_set( ) value, andthe number of points in the intersection is precisely the number ofdifferent node_id sets in which that pair is seen. This means that,except for the clustering step (which is often quadratic in the size ofthe sets S(d), but whose size may be controlled by the choice of cover),all of the other steps in the graph construction algorithm may be linearin the size of S, and may be computed quite efficiently.

In step 816, the visualization engine 322 generates the interactivevisualization of interconnected nodes (e.g., nodes and edges displayedin FIGS. 10 and 11).

It will be appreciated that it is possible, in some embodiments, to makesense in a fairly deep way of connections between various ref( ) mapsand/or choices of clustering. Further, in addition to computing edges(pairs of nodes), the embodiments described herein may be extended tocompute triples of nodes, etc. For example, the analysis module 320 maycompute simplicial complexes of any dimension (by a variety of rules) onnodes, and apply techniques from homology theory to the graphs to helpusers understand a structure in an automatic (or semi-automatic) way.

Further, it will be appreciated that uniform intervals in the coveringmay not always be a good choice. For example, if the points areexponentially distributed with respect to a given filter, uniformintervals can fail—in such case adaptive interval sizing may yielduniformly-sized S(d) sets, for instance.

Further, in various embodiments, an interface may be used to encodetechniques for incorporating third-party extensions to data access anddisplay techniques. Further, an interface may be used to for third-partyextensions to underlying infrastructure to allow for new methods forgenerating coverings, and defining new reference spaces.

FIG. 9 is an exemplary interactive visualization 900 in someembodiments. The display of the interactive visualization may beconsidered a “graph” in the mathematical sense. The interactivevisualization comprises of two types of objects: nodes (e.g., nodes 902and 906) (the colored balls) and the edges (e.g., edge 904) (the blacklines). The edges connect pairs of nodes (e.g., edge 904 connects node902 with node 906). As discussed herein, each node may represent acollection of data points (rows in the database identified by the user).In one example, connected nodes tend to include data points which are“similar to” (e.g., clustered with) each other. The collection of datapoints may be referred to as being “in the node.” The interactivevisualization may be two-dimensional, three-dimensional, or acombination of both.

In various embodiments, connected nodes and edges may form a graph orstructure. There may be multiple graphs in the interactivevisualization. In one example, the interactive visualization may displaytwo or more unconnected structures of nodes and edges.

The visual properties of the nodes and edges (such as, but not limitedto, color, stroke color, text, texture, shape, coordinates of the nodeson the screen) can encode any data based property of the data pointswithin each node. For example, coloring of the nodes and/or the edgesmay indicate (but is not limited to) the following:

-   -   Values of fields or filters    -   Any general functions of the data in the nodes (e.g., if the        data were unemployment rates by state, then GDP of the states        may be identifiable by color the nodes)    -   Number of data points in the node

The interactive visualization 900 may contain a “color bar” 910 whichmay comprise a legend indicating the coloring of the nodes (e.g., balls)and may also identify what the colors indicate. For example, in FIG. 9,color bar 910 indicates that color is based on the density filter withblue (on the far left of the color bar 910) indicating “4.99e+03” andred (on the far right of the color bar 910) indicating “1.43e+04.” Ingeneral this might be expanded to show any other legend by which nodesand/or edges are colored. It will be appreciated that the, In someembodiments, the user may control the color as well as what the color(and/or stroke color, text, texture, shape, coordinates of the nodes onthe screen) indicates.

The user may also drag and drop objects of the interactive visualization900. In various embodiments, the user may reorient structures of nodesand edges by dragging one or more nodes to another portion of theinteractive visualization (e.g., a window). In one example, the user mayselect node 902, hold node 902, and drag the node across the window. Thenode 902 will follow the user's cursor, dragging the structure of edgesand/or nodes either directly or indirectly connected to the node 902. Insome embodiments, the interactive visualization 900 may depict multipleunconnected structures. Each structure may include nodes, however, noneof the nodes of either structure are connected to each other. If theuser selects and drags a node of the first structure, only the firststructure will be reoriented with respect to the user action. The otherstructure will remain unchanged. The user may wish to reorient thestructure in order to view nodes, select nodes, and/or better understandthe relationships of the underlying data.

In one example, a user may drag a node to reorient the interactivevisualization (e.g., reorient the structure of nodes and edges). Whilethe user selects and/or drags the node, the nodes of the structureassociated with the selected node may move apart from each other inorder to provide greater visibility. Once the user lets go (e.g.,deselects or drops the node that was dragged), the nodes of thestructure may continue to move apart from each other.

In various embodiments, once the visualization module 322 generates theinteractive display, the depicted structures may move by spreading outthe nodes from each other. In one example, the nodes spread from eachother slowly allowing the user to view nodes distinguish from each otheras well as the edges. In some embodiments, the visualization module 322optimizes the spread of the nodes for the user's view. In one example,the structure(s) stop moving once an optimal view has been reached.

It will be appreciated that the interactive visualization 900 mayrespond to gestures (e.g., multitouch), stylus, or other interactionsallowing the user to reorient nodes and edges and/or interacting withthe underlying data.

The interactive visualization 900 may also respond to user actions suchas when the user drags, clicks, or hovers a mouse cursor over a node. Insome embodiments, when the user selects a node or edge, node informationor edge information may be displayed. In one example, when a node isselected (e.g., clicked on by a user with a mouse or a mouse cursorhovers over the node), a node information box 908 may appear thatindicates information regarding the selected node. In this example, thenode information box 908 indicates an ID, box ID, number of elements(e.g., data points associated with the node), and density of the dataassociated with the node.

The user may also select multiple nodes and/or edges by clickingseparate on each object, or drawing a shape (such as a box) around thedesired objects. Once the objects are selected, a selection informationbox 912 may display some information regarding the selection. Forexample, selection information box 912 indicates the number of nodesselected and the total points (e.g., data points or elements) of theselected nodes.

The interactive visualization 900 may also allow a user to furtherinteract with the display. Color option 914 allows the user to displaydifferent information based on color of the objects. Color option 914 inFIG. 9 is set to filter_Density, however, other filters may be chosenand the objects re-colored based on the selection. It will beappreciated that the objects may be colored based on any filter,property of data, or characterization. When a new option is chosen inthe color option 914, the information and/or colors depicted in thecolor bar 910 may be updated to reflect the change.

Layout checkbox 914 may allow the user to anchor the interactivevisualization 900. In one example, the layout checkbox 914 is checkedindicating that the interactive visualization 900 is anchored. As aresult, the user will not be able to select and drag the node and/orrelated structure. Although other functions may still be available, thelayout checkbox 914 may help the user keep from accidentally movingand/or reorienting nodes, edges, and/or related structures. It will beappreciated that the layout checkbox 914 may indicate that theinteractive visualization 900 is anchored when the layout checkbox 914is unchecked and that when the layout checkbox 914 is checked theinteractive visualization 900 is no longer anchored.

The change parameters button 918 may allow a user to change theparameters (e.g., add/remove filters and/or change the resolution of oneor more filters). In one example, when the change parameters button 918is activated, the user may be directed back to the metric and filterselection interface window 600 (see FIG. 6) which allows the user to addor remove filters (or change the metric). The user may then view thefilter parameter interface 700 (see FIG. 7) and change parameters (e.g.,intervals and overlap) for one or more filters. The analysis module 320may then re-analyze the data based on the changes and display a newinteractive visualization 900 without again having to specify the datasets, filters, etc.

The find ID's button 920 may allow a user to search for data within theinteractive visualization 900. In one example, the user may click thefind ID's button 920 and receive a window allowing the user to identifydata or identify a range of data. Data may be identified by ID orsearching for the data based on properties of data and/or metadata. Ifdata is found and selected, the interactive visualization 900 mayhighlight the nodes associated with the selected data. For example,selecting a single row or collection of rows of a database orspreadsheet may produce a highlighting of nodes whose correspondingpartial cluster contains any element of that selection.

In various embodiments, the user may select one or more objects andclick on the explain button 922 to receive in-depth informationregarding the selection. In some embodiments, when the user selects theexplain button 922, the information about the data from which theselection is based may be displayed. The function of the explain button922 is further discussed with regard to FIG. 10.

In various embodiments, the interactive visualization 900 may allow theuser to specify and identify subsets of interest, such as outputfiltering, to remove clusters or connections which are too small orotherwise uninteresting. Further, the interactive visualization 900 mayprovide more general coloring and display techniques, including, forexample, allowing a user to highlight nodes based on a user-specifiedpredicate, and coloring the nodes based on the intensity ofuser-specified weighting functions.

The interactive visualization 900 may comprise any number of menu items.The “Selection” menu may allow the following functions:

-   -   Select singletons (select nodes which are not connected to other        nodes)    -   Select all (selects all the nodes and edges)    -   Select all nodes (selects all nodes)    -   Select all edges    -   Clear selection (no selection)    -   Invert Selection (selects the complementary set of nodes or        edges)    -   Select “small” nodes (allows the user to threshold nodes based        on how many points they have)    -   Select leaves (selects all nodes which are connected to long        “chains” in the graph)    -   Remove selected nodes    -   Show in a table (shows the selected nodes and their associated        data in a table)    -   Save selected nodes (saves the selected data to whatever format        the user chooses. This may allow the user to subset the data and        create new datasources which may be used for further analysis.)

In one example of the “show in a table” option, information from aselection of nodes may be displayed. The information may be specific tothe origin of the data. In various embodiments, elements of a databasetable may be listed, however, other methods specified by the user mayalso be included. For example, in the case of microarray data from geneexpression data, heat maps may be used to view the results of theselections.

The interactive visualization 900 may comprise any number of menu items.The “Save” menu may allow may allow the user to save the whole output ina variety of different formats such as (but not limited to):

-   -   Image files (PNG/JPG/PDF/SVG etc.)    -   Binary output (The interactive output is saved in the binary        format. The user may reopen this file at any time to get this        interactive window again)        In some embodiments, graphs may be saved in a format such that        the graphs may be used for presentations. This may include        simply saving the image as a pdf or png file, but it may also        mean saving an executable .xml file, which may permit other        users to use the search and save capability to the database on        the file without having to recreate the analysis.

In various embodiments, a relationship between a first and a secondanalysis output/interactive visualization for differing values of theinterval length and overlap percentage may be displayed. The formalrelationship between the first and second analysis output/interactivevisualization may be that when one cover refines the next, there is amap of simplicial complexes from the output of the first to the outputof the second. This can be displayed by applying a restricted form of athree-dimensional graph embedding algorithm, in which a graph is theunion of the graphs for the various parameter values and in which theconnections are the connections in the individual graphs as well asconnections from one node to its image in the following graph. Theconstituent graphs may be placed in its own plane in 3D space. In someembodiments, there is a restriction that each constituent graph remainwithin its associated plane. Each constituent graph may be displayedindividually, but a small change of parameter value may result in thevisualization of the adjacent constituent graph. In some embodiments,nodes in the initial graph will move to nodes in the next graph, in areadily visualizable way.

FIG. 10 is an exemplary interactive visualization 1000 displaying anexplain information window 1002 in some embodiments. In variousembodiments, the user may select a plurality of nodes and click on theexplain button. When the explain button is clicked, the explaininformation window 1002 may be generated. The explain information window1002 may identify the data associated with the selected object(s) aswell as information (e.g., statistical information) associated with thedata.

In some embodiments, the explain button allows the user to get a sensefor which fields within the selected data fields are responsible for“similarity” of data in the selected nodes and the differentiatingcharacteristics. There can be many ways of scoring the data fields. Theexplain information window 1002 (i.e., the scoring window in FIG. 10) isshown along with the selected nodes. The highest scoring fields maydistinguish variables with respect to the rest of the data.

In one example, the explain information window 1002 indicates that datafrom fields day0-day6 has been selected. The minimum value of the datain all of the fields is 0. The explain information window 1002 alsoindicates the maximum values. For example, the maximum value of all ofthe data associated with the day0 field across all of the points of theselected nodes is 0.353. The average (i.e., mean) of all of the dataassociated with the day0 field across all of the points of the selectednodes is 0.031. The score may be a relative (e.g., normalized) valueindicating the relative function of the filter; here, the score mayindicate the relative density of the data associated with the day0 fieldacross all of the points of the selected nodes. It will be appreciatedthat any information regarding the data and/or selected nodes may appearin the explain information window 1002.

It will be appreciated that the data and the interactive visualization1000 may be interacted with in any number of ways. The user may interactwith the data directly to see where the graph corresponds to the data,make changes to the analysis and view the changes in the graph, modifythe graph and view changes to the data, or perform any kind ofinteraction.

FIG. 11 is a flowchart 1200 of functionality of the interactivevisualization in some embodiments. In step 1202, the visualizationengine 322 receives the analysis from the analysis module 320 and graphsnodes as balls and edges as connectors between balls 1202 to createinteractive visualization 900 (see FIG. 9).

In step 1204, the visualization engine 322 determines if the user ishovering a mouse cursor (or has selected) a ball (i.e., a node). If theuser is hovering a mouse cursor over a ball or selecting a ball, theninformation is displayed regarding the data associated with the ball. Inone example, the visualization engine 322 displays a node informationwindow 908.

If the visualization engine 322 does not determine that the user ishovering a mouse cursor (or has selected) a ball, then the visualizationengine 322 determines if the user has selected balls on the graph (e.g.,by clicking on a plurality of balls or drawing a box around a pluralityof balls). If the user has selected balls on the graph, thevisualization engine 322 may highlight the selected balls on the graphin step 1110. The visualization engine 322 may also display informationregarding the selection (e.g., by displaying a selection informationwindow 912). The user may also click on the explain button 922 toreceive more information associated with the selection (e.g., thevisualization engine 322 may display the explain information window1002).

In step 1112, the user may save the selection. For example, thevisualization engine 322 may save the underlying data, selected metric,filters, and/or resolution. The user may then access the savedinformation and create a new structure in another interactivevisualization 900 thereby allowing the user to focus attention on asubset of the data.

If the visualization engine 322 does not determine that the user hasselected balls on the graph, the visualization engine 322 may determineif the user selects and drags a ball on the graph in step 1114. If theuser selects and drags a ball on the graph, the visualization engine 322may reorient the selected balls and any connected edges and balls basedon the user's action in step 1116. The user may reorient all or part ofthe structure at any level of granularity.

It will be appreciated that although FIG. 11 discussed the user hoveringover, selecting, and/or dragging a ball, the user may interact with anyobject in the interactive visualization 900 (e.g., the user may hoverover, select, and/or drag an edge). The user may also zoom in or zoomout using the interactive visualization 900 to focus on all or a part ofthe structure (e.g., one or more balls and/or edges).

Further, although balls are discussed and depicted in FIGS. 9-11, itwill be appreciated that the nodes may be any shape and appear as anykind of object. Further, although some embodiments described hereindiscuss an interactive visualization being generated based on the outputof algebraic topology, the interactive visualization may be generatedbased on any kind of analysis and is not limited.

For years, researchers have been collecting huge amounts of data onbreast cancer, yet we are still battling the disease. Complexity, ratherthan quantity, is one of the fundamental issues in extracting knowledgefrom data. A topological data exploration and visualization platform mayassist the analysis and assessment of complex data. In variousembodiments, a predictive and visual cancer map generated by thetopological data exploration and visualization platform may assistphysicians to determine treatment options.

In one example, a breast cancer map visualization may be generated basedon the large amount of available information already generated by manyresearchers. Physicians may send biopsy data directly to a cloud-basedserver which may localize a new patient's data within the breast cancermap visualization. The breast cancer map visualization may be annotated(e.g., labeled) such that the physician may view outcomes of patientswith similar profiles as well as different kinds of statisticalinformation such as survival probabilities. Each new data point from apatient may be incorporated into the breast cancer map visualization toimprove accuracy of the breast cancer map visualization over time.

Although the following examples are largely focused on cancer mapvisualizations, it will be appreciated that at least some of theembodiments described herein may apply to any biological condition andnot be limited to cancer and/or disease. For example, some embodiments,may apply to different industries.

FIG. 12 is a flowchart for generating a cancer map visualizationutilizing biological data of a plurality of patients in someembodiments. In various embodiments, the processing of data anduser-specified options is motivated by techniques from topology and, insome embodiments, algebraic topology. As discussed herein, thesetechniques may be robust and general. In one example, these techniquesapply to almost any kind of data for which some qualitative idea of“closeness” or “similarity” exists. It will be appreciated that theimplementation of techniques described herein may apply to any level ofgenerality.

In various embodiments, a cancer map visualization is generated usinggenomic data linked to clinical outcomes (i.e., medical characteristics)which may be used by physicians during diagnosis and/or treatment.Initially, publicly available data sets may be integrated to constructthe topological map visualizations of patients (e.g., breast cancerpatients). It will be appreciated that any private, public, orcombination of private and public data sets may be integrated toconstruct the topological map visualizations. A map visualization may bebased on biological data such as, but not limited to, gene expression,sequencing, and copy number variation. As such, the map visualizationmay comprise many patients with many different types of collected data.Unlike traditional methods of analysis where distinct studies of breastcancer appear as separate entities, the map visualization may fusedisparate data sets while utilizing many datasets and data types.

In various embodiments, a new patient may be localized on the mapvisualization. With the map visualization for subtypes of a particulardisease and a new patient diagnosed with the disease, point(s) may belocated among the data points used in computing the map visualization(e.g., nearest neighbor) which is closest to the new patient point. Thenew patient may be labeled with nodes in the map visualizationcontaining the closest neighbor. These nodes may be highlighted to givea physician the location of the new patient among the patients in thereference data set. The highlighted nodes may also give the physicianthe location of the new patient relative to annotated disease subtypes.Nearest neighbor is further described in U.S. Non-Provisional patentapplication Ser. No. 13/648,237 filed Oct. 9, 2012 and entitled “Systemsand Methods for Mapping New Patient Information to Historic Outcomes forTreatment Assistance,” the entirety of which is incorporated herein byreference.

The visualization map may be interactive and/or searchable in real-timethereby potentially enabling extended analysis and providing speedyinsight into treatment.

In step 1202, biological data and clinical outcomes of previous patientsmay be received. The clinical outcomes may be medical characteristics.Biological data is any data that may represent a condition (e.g., amedical condition) of a person. Biological data may include any healthrelated, medical, physical, physiological, pharmaceutical dataassociated with one or more patients. In one example, biological datamay include measurements of gene expressions for any number of genes. Inanother example, biological data may include sequencing information(e.g., RNA sequencing).

In various embodiments, biological data for a plurality of patients maybe publicly available. For example, various medical health facilitiesand/or public entities may provide gene expression data for a variety ofpatients. In addition to the biological data, information regarding anynumber of clinical outcomes, treatments, therapies, diagnoses and/orprognoses may also be provided. It will be appreciated that any kind ofinformation may be provided in addition to the biological data.

The biological data, in one example, may be similar to data S asdiscussed with regard to step 802 of FIG. 8. The biological data mayinclude ID fields that identify patients and data fields that arerelated to the biological information (e.g., gene expressionmeasurements).

FIG. 13 is an exemplary data structure 1302 including biological data1304 a-1304 y for a number of patients 1308 a-1308 n that may be used togenerate the cancer map visualization in some embodiments. Column 1302represents different patient identifiers for different patients. Thepatient identifiers may be any identifier.

At least some biological data may be contained within gene expressionmeasurements 1304 a-1304 y. In FIG. 13, “y” represents any number. Forexample, there may be 50,000 or more separate columns for different geneexpressions related to a single patient or related to one or moresamples from a patient. It will be appreciated that column 1304 a mayrepresent a gene expression measurement for each patient (if any forsome patients) associated with the patient identifiers in column 1302.The column 1304 b may represent a gene expression measurement of one ormore genes that are different than that of column 1304 a. As discussed,there may be any number of columns representing different geneexpression measurements.

Column 1306 may include any number of clinical outcomes, prognoses,diagnoses, reactions, treatments, and/or any other informationassociated with each patient. All or some of the information containedin column 1306 may be displayed (e.g., by a label or an annotation thatis displayed on the visualization or available to the user of thevisualization via clicking) on or for the visualization.

Rows 1308 a-1308 n each contains biological data associated with thepatient identifier of the row. For example, gene expressions in row 1308a are associated with patient identifier P1. As similarly discussed withregard to “y” herein, “n” represents any number. For example, there maybe 100,000 or more separate rows for different patients.

It will be appreciated that there may be any number of data structuresthat contain any amount of biological data for any number of patients.The data structure(s) may be utilized to generate any number of mapvisualizations.

In step 1204, the analysis server may receive a filter selection. Insome embodiments, the filter selection is a density estimation function.It will be appreciated that the filter selection may include a selectionof one or more functions to generate a reference space.

In step 1206, the analysis server performs the selected filter(s) on thebiological data of the previous patients to map the biological data intoa reference space. In one example, a density estimation function, whichis well known in the art, may be performed on the biological data (e.g.,data associated with gene expression measurement data 1304 a-1304 y) torelate each patient identifier to one or more locations in the referencespace (e.g., on a real line).

In step 1208, the analysis server may receive a resolution selection.The resolution may be utilized to identify overlapping portions of thereference space (e.g., a cover of the reference space R) in step 1210.

As discussed herein, the cover of R may be a finite collection of opensets (in the metric of R) such that every point in R lies in at leastone of these sets. In various examples, R is k-dimensional Euclideanspace, where k is the number of filter functions. It will be appreciatedthat the cover of the reference space R may be controlled by the numberof intervals and the overlap identified in the resolution (e.g., seeFIG. 7). For example, the more intervals, the finer the resolution in S(e.g., the similarity space of the received biological data)—that is,the fewer points in each S(d), but the more similar (with respect to thefilters) these points may be. The greater the overlap, the more timesthat clusters in S(d) may intersect clusters in S(e)—this means thatmore “relationships” between points may appear, but, in someembodiments, the greater the overlap, the more likely that accidentalrelationships may appear.

In step 1212, the analysis server receives a metric to cluster theinformation of the cover in the reference space to partition S(d). Inone example, the metric may be a Pearson Correlation. The clusters mayform the groupings (e.g., nodes or balls). Various cluster means may beused including, but not limited to, a single linkage, average linkage,complete linkage, or k-means method.

As discussed herein, in some embodiments, the analysis module 320 maynot cluster two points unless filter values are sufficiently “related”(recall that while normally related may mean “close,” the cover mayimpose a much more general relationship on the filter values, such asrelating two points s and t if ref(s) and ref(t) are sufficiently closeto the same circle in the plane where ref( ) represents one or morefilter functions). The output may be a simplicial complex, from whichone can extract its 1-skeleton. The nodes of the complex may be partialclusters, (i.e., clusters constructed from subsets of S specified as thepreimages of sets in the given covering of the reference space R).

In step 1214, the analysis server may generate the visualization mapwith nodes representing clusters of patient members and edges betweennodes representing common patient members. In one example, the analysisserver identifies nodes which are associated with a subset of thepartition elements of all of the S(d) for generating an interactivevisualization.

As discussed herein, for example, suppose that S={1, 2, 3, 4}, and thecover is C1, C2, C3. Suppose cover C1 contains {1, 4}, C2 contains{1,2}, and C3 contains {1,2,3,4}. If 1 and 2 are close enough to beclustered, and 3 and 4 are, but nothing else, then the clustering forS(1) may be {1}, {4}, and for S(2) it may be {1,2}, and for S(3) it maybe {1,2}, {3,4}. So the generated graph has, in this example, at mostfour nodes, given by the sets {1}, {4}, {1, 2}, and {3, 4} (note that{1, 2} appears in two different clusterings). Of the sets of points thatare used, two nodes intersect provided that the associated node setshave a non-empty intersection (although this could easily be modified toallow users to require that the intersection is “large enough” either inabsolute or relative terms).

As a result of clustering, member patients of a grouping may sharebiological similarities (e.g., similarities based on the biologicaldata).

The analysis server may join clusters to identify edges (e.g.,connecting lines between nodes). Clusters joined by edges (i.e.,interconnections) share one or more member patients. In step 1216, adisplay may display a visualization map with attributes based on theclinical outcomes contained in the data structures (e.g., see FIG. 13regarding clinical outcomes). Any labels or annotations may be utilizedbased on information contained in the data structures. For example,treatments, prognoses, therapies, diagnoses, and the like may be used tolabel the visualization. In some embodiments, the physician or otheruser of the map visualization accesses the annotations or labels byinteracting with the map visualization.

The resulting cancer map visualization may reveal interactions andrelationships that were obscured, untested, and/or previously notrecognized.

FIG. 14 is an exemplary visualization displaying the cancer mapvisualization 1400 in some embodiments. The cancer map visualization1400 represents a topological network of cancer patients. The cancer mapvisualization 1400 may be based on publicly and/or privately availabledata.

In various embodiments, the cancer map visualization 1400 is createdusing gene expression profiles of excised tumors. Each node (i.e., ballor grouping displayed in the map visualization 1400) contains a subsetof patients with similar genetic profiles.

As discussed herein, one or more patients (i.e., patient members of eachnode or grouping) may occur in multiple nodes. A patient may share asimilar genetic profile with multiple nodes or multiple groupings. Inone example, of 50,000 different gene expressions of the biologicaldata, multiple patients may share a different genetic profiles (e.g.,based on different gene expression combinations) with differentgroupings. When a patient shares a similar genetic profile withdifferent groupings or nodes, the patient may be included within thegroupings or nodes.

The cancer map visualization 1400 comprises groupings andinterconnections that are associated with different clinical outcomes.All or some of the clinical outcomes may be associated with thebiological data that generated the cancer map visualization 1400. Thecancer map visualization 1400 includes groupings associated withsurvivors 1402 and groupings associated with non-survivors 1404. Thecancer map visualization 1400 also includes different groupingsassociated with estrogen receptor positive non-survivors 1406, estrogenreceptor negative non-survivors 1408, estrogen receptor positivesurvivors 1410, and estrogen receptor negative survivors 1412.

In various embodiments, when one or more patients are members of two ormore different nodes, the nodes are interconnected by an edge (e.g., aline or interconnection). If there is not an edge between the two nodes,then there are no common member patients between the two nodes. Forexample, grouping 1414 shares at least one common member patient withgrouping 1418. The intersection of the two groupings is represented byedge 1416. As discussed herein, the number of shared member patients ofthe two groupings may be represented in any number of ways includingcolor of the interconnection, color of the groupings, size of theinterconnection, size of the groupings, animations of theinterconnection, animations of the groupings, brightness, or the like.In some embodiments, the number and/or identifiers of shared memberpatients of the two groupings may be available if the user interactswith the groupings 1414 and/or 1418 (e.g., draws a box around the twogroupings and the interconnection utilizing an input device such as amouse).

In various embodiments, a physician, on obtaining some data on a breasttumor, direct the data to an analysis server (e.g., analysis server 208over a network such as the Internet) which may localize the patientrelative to one or more groupings on the cancer map visualization 1400.The context of the cancer map visualization 1400 may enable thephysician to assess various possible outcomes (e.g., proximity ofrepresentation of new patient to the different associations of clinicaloutcomes).

FIG. 15 is a flowchart of for positioning new patient data relative to acancer map visualization in some embodiments. In step 1502, newbiological data of a new patient is received. In various embodiments, aninput module 314 of an analysis server (e.g., analysis server 208 ofFIGS. 1 and 2) may receive biological data of a new patient from aphysician or medical facility that performed analysis of one or moresamples to generate the biological data. The biological data may be anydata that represents a biological data of the new patient including, forexample, gene expressions, sequencing information, or the like.

In some embodiments, the analysis server 208 may comprise a new patientdistance module and a location engine. In step 1504, the new patientdistance module determines distances between the biological data of eachpatient of the cancer map visualization 1600 and the new biological datafrom the new patient. For example, the previous biological data that wasutilized in the generation of the cancer map visualization 1600 may bestored in mapped data structures. Distances may be determined betweenthe new biological data of the new patient and each of the previouspatient's biological data in the mapped data structure.

It will be appreciated that distances may be determined in any number ofways using any number of different metrics or functions. Distances maybe determined between the biological data of the previous patients andthe new patients. For example, a distance may be determined between afirst gene expression measurement of the new patient and each (or asubset) of the first gene expression measurements of the previouspatients (e.g., the distance between GI of the new patient and GI ofeach previous patient may be calculated). Distances may be determinedbetween all (or a subset of) other gene expression measurements of thenew patient to the gene expression measurements of the previouspatients.

In various embodiments, a location of the new patient on the cancer mapvisualization 1600 may be determined relative to the other memberpatients utilizing the determined distances.

In step 1506, the new patient distance module may compare distancesbetween the patient members of each grouping to the distances determinedfor the new patient. The new patient may be located in the grouping ofpatient members that are closest in distance to the new patient. In someembodiments, the new patient location may be determined to be within agrouping that contains the one or more patient members that are closestto the new patient (even if other members of the grouping have longerdistances with the new patient). In some embodiments, this step isoptional.

In various embodiments, a representative patient member may bedetermined for each grouping. For example, some or all of the patientmembers of a grouping may be averaged or otherwise combined to generatea representative patient member of the grouping (e.g., the distancesand/or biological data of the patient members may be averaged oraggregated). Distances may be determined between the new patientbiological data and the averaged or combined biological data of one ormore representative patient members of one or more groupings. Thelocation engine may determine the location of the new patient based onthe distances. In some embodiments, once the closest distance betweenthe new patient and the representative patient member is found,distances may be determined between the new patient and the individualpatient members of the grouping associated with the closestrepresentative patient member.

In optional step 1508, a diameter of the grouping with the one or moreof the patient members that are closest to the new patient (based on thedetermined distances) may be determined. In one example, the diametersof the groupings of patient members closest to the new patient arecalculated. The diameter of the grouping may be a distance between twopatient members who are the farthest from each other when compared tothe distances between all patient members of the grouping. If thedistance between the new patient and the closest patient member of thegrouping is less than the diameter of the grouping, the new patient maybe located within the grouping. If the distance between the new patientand the closest patient member of the grouping is greater than thediameter of the grouping, the new patient may be outside the grouping(e.g., a new grouping may be displayed on the cancer map visualizationwith the new patient as the single patient member of the grouping). Ifthe distance between the new patient and the closest patient member ofthe grouping is equal to the diameter of the grouping, the new patientmay be placed within or outside the grouping.

It will be appreciated that the determination of the diameter of thegrouping is not required in determining whether the new patient locationis within or outside of a grouping. In various embodiments, adistribution of distances between member patients and between memberpatients and the new patient is determined. The decision to locate thenew patient within or outside of the grouping may be based on thedistribution. For example, if there is a gap in the distribution ofdistances, the new patient may be separated from the grouping (e.g., asa new grouping). In some embodiments, if the gap is greater than apreexisting threshold (e.g., established by the physician, other user,or previously programmed), the new patient may be placed in a newgrouping that is placed relative to the grouping of the closest memberpatients. The process of calculating the distribution of distances ofcandidate member patients to determine whether there may be two or moregroupings may be utilized in generation of the cancer map visualization(e.g., in the process as described with regard to FIG. 12). It will beappreciated that there may be any number of ways to determine whether anew patient should be included within a grouping of other patientmembers.

In step 1510, the location engine determines the location of the newpatient relative to the member patients and/or groupings of the cancermap visualization. The new location may be relative to the determineddistances between the new patient and the previous patients. Thelocation of the new patient may be part of a previously existinggrouping or may form a new grouping.

In some embodiments, the location of the new patient with regard to thecancer map visualization may be performed locally to the physician. Forexample, the cancer map visualization 1400 may be provided to thephysician (e.g., via digital device). The physician may load the newpatient's biological data locally and the distances may be determinedlocally or via a cloud-based server. The location(s) associated with thenew patient may be overlaid on the previously existing cancer mapvisualization either locally or remotely.

Those skilled in the art will appreciate that, in some embodiments, theprevious state of the cancer map visualization (e.g., cancer mapvisualization 1400) may be retained or otherwise stored and a new cancermap visualization generated utilizing the new patient biological data(e.g., in a method similar to that discussed with regard to FIG. 12).The newly generated map may be compared to the previous state and thedifferences may be highlighted thereby, in some embodiments,highlighting the location(s) associated with the new patient. In thisway, distances may be not be calculated as described with regard to FIG.15, but rather, the process may be similar to that as previouslydiscussed.

FIG. 16 is an exemplary visualization displaying the cancer mapincluding positions for three new cancer patients in some embodiments.The cancer map visualization 1400 comprises groupings andinterconnections that are associated with different clinical outcomes asdiscussed with regard to FIG. 14. All or some of the clinical outcomesmay be associated with the biological data that generated the cancer mapvisualization 1400. The cancer map visualization 1400 includes differentgroupings associated with survivors 1402, groupings associated withnon-survivors 1404, estrogen receptor positive non-survivors 1406,estrogen receptor negative non-survivors 1408, estrogen receptorpositive survivors 1410, and estrogen receptor negative survivors 1412.

The cancer map visualization 1400 includes three locations for three newbreast cancer patients. The breast cancer patient location 1602 isassociated with the clinical outcome of estrogen receptor positivesurvivors. The breast cancer patient location 1604 is associated withthe clinical outcome of estrogen receptor negative survivors.Unfortunately, breast cancer patient location 1606 is associated withestrogen receptor negative non-survivors. Based on the locations, aphysician may consider different diagnoses, prognoses, treatments, andtherapies to maintain or attempt to move the breast cancer patient to adifferent location utilizing the cancer map visualization 1400.

In some embodiments, the physician may assess the underlying biologicaldata associated with any number of member patients of any number ofgroupings to better understand the genetic similarities and/ordissimilarities. The physician may utilize the information to makebetter informed decisions.

The patient location 1604 is highlighted on the cancer map visualization1400 as active (e.g., selected by the physician). It will be appreciatedthat the different locations may be of any color, size, brightness,and/or animated to highlight the desired location(s) for the physician.Further, although only one location is identified for three differentbreast cancer patients, any of the breast cancer patients may havemultiple locations indicating different genetic similarities.

It will be appreciated that the cancer map visualization 1400 may beupdated with new information at any time. As such, as new patients areadded to the cancer map visualization 1400, the new data updates thevisualization such that as future patients are placed in the map, themap may already include the updated information. As new informationand/or new patient data is added to the cancer map visualization 1400,the cancer map visualization 1400 may improve as a tool to better informphysicians or other medical professionals.

In various embodiments, the cancer map visualization 1400 may trackchanges in patients over time. For example, updates to a new patient maybe visually tracked as changes in are measured in the new patient'sbiological data. In some embodiments, previous patient data is similarlytracked which may be used to determine similarities of changes based oncondition, treatment, and/or therapies, for example. In variousembodiments, velocity of change and/or acceleration of change of anynumber of patients may be tracked over time using or as depicted on thecancer map visualization 1400. Such depictions may assist the treatingphysician or other personnel related to the treating physician to betterunderstand changes in the patient and provide improved, current, and/orupdated diagnoses, prognoses, treatments, and/or therapies.

FIG. 17 is a flowchart of utilization the visualization and positioningof new patient data in some embodiments. In various embodiments, aphysician may collect amounts of genomic information from tumors removedfrom a new patient, input the data (e.g., upload the data to an analysisserver), and receive a map visualization with a location of the newpatient. The new patient's location within the map may offer thephysician new information about the similarities to other patients. Insome embodiments, the map visualization may be annotated so that thephysician may check the outcomes of previous patients in a given regionof the map visualization are distributed and then use the information toassist in decision-making for diagnosis, treatment, prognosis, and/ortherapy.

In step 1702, a medical professional or other personnel may remove asample from a patient. The sample may be of a tumor, blood, or any otherbiological material. In one example, a medical professional performs atumor excision. Any number of samples may be taken from a patient.

In step 1704, the sample(s) may be provided to a medical facility todetermine new patient biological data. In one example, the medicalfacility measures genomic data such as gene expression of a number ofgenes or protein levels.

In step 1706, the medical professional or other entity associated withthe medical professional may receive the new patient biological databased on the sample(s) from the new patient. In one example, a physicianmay receive the new patient biological data. The physician may provideall or some of the new patient biological data to an analysis serverover the Internet (e.g., the analysis server may be a cloud-basedserver). In some embodiments, the analysis server is the analysis server208 of FIG. 1. In some embodiments, the medical facility that determinesthe new patient biological data provides the biological data in anelectronic format which may be uploaded to the analysis server. In someembodiments, the medical facility that determines the new patientbiological data (e.g., the medical facility that measures the genomicdata) provide the biological data to the analysis server at the requestof the physician or others associated with the physician. It will beappreciated that the biological data may be provided to the analysisserver in any number of ways.

The analysis server may be any digital device and may not be limited toa digital device on a network. In some embodiments, the physician mayhave access to the digital device. For example, the analysis server maybe a table, personal computer, local server, or any other digitaldevice.

Once the analysis server receives the biological data of the newpatient, the new patient may be localized in the map visualization andthe information may be sent back to the physician in step 1708. Thevisualization may be a map with nodes representing clusters of previouspatient members and edges between nodes representing common patientmembers. The visualization may further depict one or more locationsrelated to the biological data of the new patient.

The map visualization may be provided to the physician or otherassociated with the physician in real-time. For example, once thebiological data associated with the new patient is provided to theanalysis server, the analysis server may provide the map visualizationback to the physician or other associated with the physician within areasonably short time (e.g., within seconds or minutes). In someembodiments, the physician may receive the map visualization over anytime.

The map visualization may be provided to the physician in any number ofways. For example, the physician may receive the map visualization overany digital device such as, but not limited to, an office computer,Ipad, tablet device, media device, smartphone, e-reader, or laptop.

In step 1710, the physician may assess possible different clinicaloutcomes based on the map visualization. In one example, the map-aidedphysician may make decisions on therapy and treatments depending onwhere the patient lands on the visualization (e.g., survivor ornon-survivor). The map visualization may include annotations or labelsthat identify one or more sets of groupings and interconnections asbeing associated with one or more clinical outcomes. The physician mayassess possible clinical outcomes based on the position(s) on the mapassociated with the new patient.

As described above, interesting continuous functions on a metric space(e.g., a similarity space) allow the application of systems and methodsdescribed herein. In various embodiments, functions may be performed ondata within the metric space to project data into the reference space.Having the function(s) to project the data from the metric space to thesimilarity space (i.e., a lens function) dependent on a small number ofcoordinates (e.g., counting a number of uses of a small collection ofwords) is a fairly simple way to achieve continuity in most metrics, andthe resulting lenses may be suitable for interpolation. However, suchlenses may be of limited use on high-dimensional data, and if theinteresting features of the space were captured in those few dimensions,there may be no point keeping the rest of the coordinates.

In practice, lenses which incorporate intrinsic properties of the metric(e.g., the function on the data to generate the metric space), such asdensity or centrality, are more likely to capture features of the space,absent special knowledge of the particular data set, than functionswhich depend on a few coordinates. One example method of dimensionalityreduction (which is a way to think of a small collection of lensesapplied jointly) are variants of “Stochastic Neighbor Embedding” (akaSNE). The underlying intuition in stochastic neighbor embedding is tomap the high dimensional space to points in a low-dimensional Euclideanspace, typically two or three dimensions, define a potential function onthe points which penalizes them for being either closer or farther apartin the embedding than they are in the high-dimensional space, and movepoints around to minimize the potential. This may be effectively like agraph-layout problem, where a (potentially) high-dimensional space, anarbitrary combinatorial graph, is to be faithfully represented by atwo-dimensional picture.

Some example methods amount to computing a global potential and thenoptimizing the placement by the same optimization techniques used inapplications of artificial neural network. These methods produce verynice pictures and the lenses can be remarkably effective with TDA, butthey may be computationally expensive. Some embodiments described hereinallow for the use of less computationally expensive layout mechanismsand methods.

FIG. 18 is a block diagram of an exemplary digital device 1800. Thedigital device 1800 comprises a processor 1802, a memory system 1804, astorage system 1806, a communication network interface 1808, an I/Ointerface 1810, and a display interface 1812 communicatively coupled toa bus 1814. The processor 1802 may be configured to execute executableinstructions (e.g., programs). In some embodiments, the processor 1802comprises circuitry or any processor capable of processing theexecutable instructions.

The memory system 1804 is any memory configured to store data. Someexamples of the memory system 1804 are storage devices, such as RAM orROM. The memory system 1804 can comprise the ram cache. In variousembodiments, data is stored within the memory system 1804. The datawithin the memory system 1804 may be cleared or ultimately transferredto the storage system 1806.

The storage system 1806 is any storage configured to retrieve and storedata. Some examples of the storage system 1806 are flash drives, harddrives, optical drives, and/or magnetic tape. In some embodiments, thedigital device 1800 includes a memory system 1804 in the form of RAM anda storage system 1806 in the form of flash data. Both the memory system1804 and the storage system 1806 comprise computer readable media whichmay store instructions or programs that are executable by a computerprocessor including the processor 1802.

The communication network interface (com. network interface) 1808 can becoupled to a communication network (e.g., communication network 204) viathe link 1816. The communication network interface 1808 may supportcommunication over an Ethernet connection, a serial connection, aparallel connection, or an ATA connection, for example. Thecommunication network interface 1808 may also support wirelesscommunication (e.g., 1802.11a/b/g/n, WiMax). It will be apparent tothose skilled in the art that the communication network interface 1808can support many wired and wireless standards.

The optional input/output (I/O) interface 1810 is any device thatreceives input from the user and output data. The optional displayinterface 1812 is any device that may be configured to output graphicsand data to a display. In one example, the display interface 1812 is agraphics adapter.

It will be appreciated by those skilled in the art that the hardwareelements of the digital device 1800 are not limited to those depicted inFIG. 18. A digital device 1800 may comprise more or less hardwareelements than those depicted. Further, hardware elements may sharefunctionality and still be within various embodiments described herein.In one example, encoding and/or decoding may be performed by theprocessor 1802 and/or a co-processor located on a GPU.

In various embodiments, data points of a data set or nodes in a graphare automatically grouped (i.e., “autogrouped”). The groupings may beapproximations of a possible maxima (e.g., a best maxima) of a givenscoring function that scores possible partitions of the original object(i.e., a collection of data points or a collection of nodes of a graph).

Autogrouping may be utilized to automatically find a collection ofsubsets of some set Y that share one or more given properties. In oneexample, autogrouping may be utilized to find a collection of subsetsthat is a partition of Y where Y is a subset of a finite metric space Xor nodes in a graph. However, it will be appreciated, in someembodiments, that the methodology described herein has no suchrequirement.

In various embodiments, a selection of possible partitions of a data set(e.g., original data set or nodes in a visualization) may be identifiedand scored. A partition is a collection of disjoint subsets of a givenset. The union of the subsets of each partition equal the entireoriginal set. A hierarchical clustering method may be utilized on theoriginal object Y to create a family of partitions of Y.

A first scoring function may score the subsets (i.e., to generate aQ_Subset score), a second scoring function may score the partitions(i.e., to generate a Q_Partition score), and a third scoring functionmay score the roots of trees coming from the hierarchical clusteringmethod (i.e., to generate a Q_Max score). The highest scoring partitionbased on any one or a combination of these scoring functions may befound for the family. The first and/or second scoring functions may beany function or combination of functions that may be able to be scored.Example scoring functions are further discussed herein.

In some embodiments, autogrouping is the process in which a highestscoring partition is identified. The highest scoring partition may bethe maximum of the given scoring function(s) of any number of subsetsfrom any number of partitions.

In some embodiments, a limited number of partitions of all possiblepartitions may be generated. In fact, in some cases, the result may bebetter if the scorer is imperfect, as at least some hierarchicalclustering algorithms generally avoid partitions with large numbers ofmiscellaneous singletons or other ugly sets which might actually be theglobal extreme for such a scoring function. It will be appreciated thatthe hierarchical clustering process may serve to condition data to onlypresent ‘good alternatives,’ and so can improve the effectiveness ofsome scorers.

Since the number of partitions for a data set is high (e.g.,(N/log(N)){circumflex over ( )}N), it may be impractical to generateevery possible partition. Unfortunately, most local improvement methodscan easily get stuck. Some techniques to generate a subset of partitionsinvolve attempting to maximize a modularity score over graph partitionsby making an initial partition and then making local changes (e.g.,moving nodes from one partition to another). Modularity is the fractionof edges that fall within given groups minus the expected such fractionif edges were distributed at random. Unfortunately, the modularitymeasure Q score may exhibit extreme degeneracies because it admits anexponential number of distinct high-scoring solutions and typicallylacks a clear global maximum. Another approach to maximizing functionson partitions by local methods is to use probabilistic techniques suchas simulated annealing. At least some embodiments described herein offera deterministic alternative that is applicable to a wide range ofscoring functions.

Subsets in one or more different partitions of those generated may beselected based, at least in part, on Q scores, further described herein.A new partition including the selected subsets may be generated or, ifall of the selected subsets are already part of a generated partition,then the preexisting partition may be selected.

FIGS. 19A-19D depict an example of determining a partition based onscoring for autogrouping in some embodiments. In an example, there is afixed space, S, of finite size. The nature of the space may be relevantonly in so far as there is a way of clustering the space and scoringsubsets. Referring to a graph G on S indicates a graph whose nodes are acollection of subsets where a node is connected to another node if andonly if the two nodes have points in common. A partition includes one ormore subsets. Each of the one or more subsets include all of theelement(s) of S. For example, partition 1902 is a partition thatincludes subsets of all elements of S. Subsets 1904 a-e include allelements of S. A union of all of the subsets 1904 a-e is the partition1902.

A forest F on S is a graph on S. A forest F is ‘atomic’ if every leaf inF is a singleton (e.g., a set with one member). FIG. 19A (i.e., F1) isan atomic forest because every leaf in F1 as depicted in FIG. 19A is asingleton. It will be appreciated that FIG. 19B (i.e., F2) is not anatomic forest since every leaf in F2 as depicted in FIG. 19B is not asingleton. For example, F2 includes leaves {A,B}, {D,E}, and {F,G}.

There is a partition R of S (in F1, {a,b,c}, {d,e,f}, {g}), called theroots, such that every set in F is reachable by a unique path from aroot. N in F is either a leaf (e.g., a singleton in an atomic forest) orit is connected to nodes which form a partition (e.g., {a,b,c}→{a,b} and{c} in F1) of N. For a non-leaf node N we denote by C(N) the children ofN. Notice the children of a leaf, namely C(leaf) is empty. We say thatF′ extends F if F and F′ have the same leaves and every node in F is anode in F′. If the two forests are not equal, then F′ contains a nodewhich is the union of one or more roots in F. Example F3 (FIG. 19C)extends F1 (FIG. 19A).

Partition P on S is subordinate to F1 if and only if every element of Pis in F1. The circled partition P1 of F4 depicted in FIG. 19D, is anexample of a subordinate partition {e.g., {a,b,c},{d,e},{f}, and {g}} toF1.

Singletons(S) are denoted as the partition formed by taking {{x}|x inS}. That is, in the example in FIG. 19D, Singletons({a, b, c, d, e, f,g})={{a},{b},{c},{d},{e}, {f},{g} }. This is the same as the set ofleaves of an atomic forest. Let U(P), where P is any collection ofsubsets of S, denote the union of all the elements of P.U(Singletons(S))==S.

Partition P′ on S is coarser than another partition P on S if and onlyif every element x′ in P′ is the union of elements x in P. In variousembodiments, every partition on S is coarser than Singletons(S), and {S}is coarser than every partition on S. For instance, {{a,b,c},{d,e,f},{g}} is a coarser partition than {{a,b},{c},{d,e},{f},{g} }.

FIG. 20 is a block diagram of an exemplary analysis server 208 includingan autogroup module 2002. The exemplary analysis server 208 depicted inFIG. 20 may be similar to the exemplary analysis server 208 depicted inFIG. 2. In exemplary embodiments, the analysis server 208 comprises aprocessor 302, input/output (I/O) interface 304, a communication networkinterface 306, a memory system 308, and a storage system 310.

The storage system 310 comprises a plurality of modules utilized byembodiments of the present invention. A module may be hardware (e.g., anASIC), software (e.g., including instructions executable by aprocessor), or a combination of both. In one embodiment, the storagesystem 310 comprises a processing module 312 which comprises an inputmodule 314, a filter module 316, a resolution module 318, an analysismodule 320, a visualization engine 322, a database storage 324, and anautogroup module 2002. Alternative embodiments of the analysis server208 and/or the storage system 310 may comprise more, less, orfunctionally equivalent components and modules.

The input module 314 may be configured to receive commands andpreferences from the user device 202 a. In various examples, the inputmodule 314 receives selections from the user which will be used toperform the analysis. The output of the analysis may be an interactivevisualization.

The input module 314 may provide the user a variety of interface windowsallowing the user to select and access a database, choose fieldsassociated with the database, choose a metric, choose one or morefilters, and identify resolution parameters for the analysis. In oneexample, the input module 314 receives a database identifier andaccesses a large multi-dimensional database. The input module 314 mayscan the database and provide the user with an interface window allowingthe user to identify an ID field. An ID field is an identifier for eachdata point. In one example, the identifier is unique. The same columnname may be present in the table from which filters are selected. Afterthe ID field is selected, the input module 314 may then provide the userwith another interface window to allow the user to choose one or moredata fields from a table of the database.

Although interactive windows may be described herein, it will beappreciated that any window, graphical user interface, and/or commandline may be used to receive or prompt a user or user device 202 a forinformation.

The filter module 316 may subsequently provide the user with aninterface window to allow the user to select a metric to be used inanalysis of the data within the chosen data fields. The filter module316 may also allow the user to select and/or define one or more filters.

The resolution module 318 may allow the user to select a resolution,including filter parameters. In one example, the user enters a number ofintervals and a percentage overlap for a filter.

The analysis module 320 may perform data analysis based on the databaseand the information provided by the user. In various embodiments, theanalysis module 320 performs an algebraic topological analysis toidentify structures and relationships within data and clusters of data.It will be appreciated that the analysis module 320 may use parallelalgorithms or use generalizations of various statistical techniques(e.g., generalizing the bootstrap to zig-zag methods) to increase thesize of data sets that can be processed. The analysis is furtherdiscussed in FIG. 8. It will be appreciated that the analysis module 320is not limited to algebraic topological analysis but may perform anyanalysis.

The visualization engine 322 generates an interactive visualizationincluding the output from the analysis module 320. The interactivevisualization allows the user to see all or part of the analysisgraphically. The interactive visualization also allows the user tointeract with the visualization. For example, the user may selectportions of a graph from within the visualization to see and/or interactwith the underlying data and/or underlying analysis. The user may thenchange the parameters of the analysis (e.g., change the metric,filter(s), or resolution(s)) which allows the user to visually identifyrelationships in the data that may be otherwise undetectable using priormeans. The interactive visualization is further described in FIGS. 9-11.

The database storage 324 is configured to store all or part of thedatabase that is being accessed. In some embodiments, the databasestorage 324 may store saved portions of the database. Further, thedatabase storage 324 may be used to store user preferences, parameters,and analysis output thereby allowing the user to perform many differentfunctions on the database without losing previous work.

The autogroup module 2002 is configured to autogroup data points of adata set or nodes in a graph. As discussed herein, the groupings may beapproximations of possible maxima of a given scoring function thatscores possible partitions of the original data object (e.g., acollection of data points or a collection of nodes of a graph). Theautogroup module 2002 may, in some embodiments, perform autogrouping ofnodes of a graph (whether a visualization is generated or not). Invarious embodiments, the autogroup module 2002 may perform autogroupingfor reference space open cover generation. The autogroup module 2002 mayautogroup any number of data points, sets of data points,representations, and/or the like. The autogroup module 2002 is furtherdiscussed in FIG. 21.

It will be appreciated that that all or part of the processing module212 may be at the user device 202 a or the database storage server 206.In some embodiments, all or some of the functionality of the processingmodule 312 may be performed by the user device 202 a.

FIG. 21 depicts an example autogroup module 2002 in some embodiments. Anautogroup module 2002 may comprise a data structure module 2102, apartition generation module 2104, scoring function modules (e.g., aQ_subset score module 2106, a Q_max score module 2108, a Q_partitionscore module 2110), a partition selection module 2112, and a datacontrol module 2114. Although the scoring function modules are discussedas including three modules, each performing a different scoringfunction, it will be appreciated that there may be any number of scoringfunction modules performing any number of scoring functions (e.g., onemodule performing a single scoring function capable of generating anynumber or type of scores). For example, the scoring functions maygenerate and/or maximize metric values of any number of metricfunctions.

In various embodiments, the data structure module 2102 receives dataincluding a plurality of sets of data. The data may be received from anynumber of digital devices.

The partition generation module 2104 (e.g., a “clumper”) forms a forestF utilizing the plurality of sets of data received by the data structuremodule 2102. For example, the partition generation module 2104 maygenerate a first partition of a forest F using the data received by thedata structure module 2102. In some embodiments, the first partition mayinclude leaves that are singletons of all elements from the data. Invarious embodiments, the first partition may include any number of setsof data. The first partition may include leaves for the forest,singletons, roots, sets of plurality of elements, and/or the like.

The partition generation module 2104 may generate the second partitionof the forest F using the first partition. For example, the secondpartition may include at least one union of at least two sets of thefirst partition. Subsequent partitions may be generated in a similarfashion (e.g., based, at least in part, on including at least one unionof at least two sets from the previous partition).

The partition generation module 2104 may generate an entire forest Fbefore scoring partitions (or sets of partitions). For example, thepartition generation module 2104 may generate the entire forest F beforeany or all of the scoring function modules score all or parts ofpartitions of the forest F.

In some embodiments, the partition generation module 2104 may generatethe entire forest F while scoring is performed or in series withpartition scoring (e.g., scoring of sets of partitions). For example,the partition generation module 2104 may generate the entire forest Fwhile any or all of the scoring function modules score all or parts ofpartitions of the forest F. In another example, the partition generationmodule 2104 may generate one or more partitions of the forest F and thenany number of the scoring function modules may score the generatedpartitions before the partition generation module 2104 generates one ormore additional partitions of the forest F.

In various embodiments, the partition generation module 2104 maygenerate a partition of a forest F based on, at least in part, scores byany number of scoring function modules of previously generatedpartition(s) (or sets of partition(s)) of the forest F.

It will be appreciated that the partition generation module 2104 may notgenerate the entire forest F but may rather terminate generatingpartitions of the forest F before the forest F is completed. Thepartition generation module 2104 may determine whether to build a newpartition of the forest F based on any number of the previouslygenerated partition(s) of the forest F and/or scoring associated withall or parts of previously generated partition(s).

As discussed herein, the partition generation module 2104 may notgenerate all possible sets of data and/or all possible partitions of theforest F.

It will be appreciated that the partition generation module 2104 mayutilize any number of hierarchical clustering techniques with techniquesdescribed herein. In one example, data and/or nodes are joined byepsilon (if 2 data subsets or nodes are within distance epsilon of eachother then they are joined together). While this example standardtechnique has traditional limitations (“fixed epsilon”) whereby a singleepsilon may be unable to break up a space in a preferable manner, byscoring each subset of a partition, we can select subsets across aforest to identify and/or generate a selected partition (e.g., byautogrouping subsets of a plurality of partitions).

One example of a hierarchical clustering technique, KNN on a finitemetric space X is to compute the K nearest neighbors for each point of anetwork graph (e.g., a visualized or non-visualized graph that includesnodes that may be coupled to one or more other nodes of the graph) with,for example, K=50. The partition generation module 2104 may start withINITIAL( ) being Singletons(X). Then at each step for 1<=k<=50, thepartition generation module 2104 may connect x to y provided x and y arein the symmetric k nearest neighbors of one another. Note that ifKNN(P,k) returns P for k<50, the partition generation module 2104 maybump k and try again instead of concluding that P is stable.

Another hierarchical clustering technique embodiment is defined on aweighted graph G (with positive weights) on a point set S. Thishierarchical clustering technique is parameterized by a pre-determinedreal number delta where 1>delta>0. The partition generation module 2104starts with delta=0 so INITIAL( ) being Singletons(S). For eachpartition P, we define wt(p,q), for p!=q in P, to be the sum of edgeweights between the nodes in the graph which are a part of the subset pand those in the subset q in G, divided by |p|*|q|. The partitiongeneration module 2104 is configured to take a partition P and make anew partition P′ by joining all pairs of subsets (a,b) (where a, b aresubsets in the partition P) when wt(a,b)>=delta*max(wt(p,q)) where themax is over all pairs of subsets p and q in the partition P.

There are any number of techniques for hierarchical clustering and anyof them can be combined with a scoring function that satisfies exampleconstraints on the scoring functions discussed herein.

The autogroup module 2002 includes the Q_Subset score module 2106, theQ_Max score module 2108, and the Q_Partition score module 2110 which mayutilize three scoring functions, respectively. The Q_Subset score module2106 calculates a Q_Subset score for subsets of one or more partitions.The Q_Max score module 2108 calculates a Q_Max score based on theQ_Subset score (e.g., calculates a maximum score for a partition basedon the Q_Subset score) for the subsets. The Q_Partition score module2110 calculates a Q_Partition score for two or more partitions of theforest utilizing at least the Q_Subset Score for the subsets.

In various embodiments, the Q_Subset score module 2106 calculatesQ_Subset scores (e.g., one for each subset of a partition). A function Qis defined on subsets of the space S and scores the properties which areto be grouped together in the autogrouping process. For instance, insome embodiments, the Q_Subset score is a modularity score on a graph(so S are the nodes in the graph). The partition selection module 2112may examine the data structure for a partition of the graph S withmaximum modularity score(s).

Modularity is one measure of the structure of networks or graphs that isappreciated by those skilled in the art. The modularity score may beused to measure strength of division of a network of nodes (e.g.,modules, groups, clusters, or communities). Modularity may be used inoptimization methods for detecting community structure in networks. Inone example, modularity is the fraction of edges of nodes in a graphthat fall within a given group minus the expected such fraction if edgeswere distributed at random. It will be appreciated that there are manydifferent methods for calculating modularity.

In one example, randomization of edges preserves a degree of eachvertex. Assume a graph with n nodes and m links (edges) such that thegraph can be partitioned into two communities using a membershipvariable s. If a node v belongs to community 1, Sv=1, or if v belongs tocommunity 2, Sv=−1. An adjacency matrix for an undirected network may berepresented by A, where Avw=0 indicates there are no edges (nointeraction) between nodes v and w. Avw=1 indicates there are Avw=1indicates there is an edge between the two.

Modularity Q may be defined as the fraction of edges that fall withingroup 1 or 2, minus the expected number of edges within groups 1 and 2for a random graph with the same node degree distribution as thenetwork.

In this example, an expected number of edges is determined usingconfiguration models. The configuration model is a randomizedrealization of a particular network. Given a network with n nodes, whereeach node v has a node degree kv, the configuration model cuts each edgeinto two halves, and then each half edge is rewired randomly with anyother half edge in the network.

For this example, assume that the total number of half edges is l_(n)

$l_{n} = {{\sum\limits_{v}k_{v}} = {2m}}$

Two randomly nodes v and w with node degrees k_(v) and k_(w)respectively are selected and half edges rewired then the expectation offull edges between v and w is equal to (Full edges between v andw)/(total number of rewiring possibilities). The expected [Number offull edges between v and w]=(k_(v)*k_(w))/l_(n)=(k_(v)k_(w))/2m.

As a result, the actual number of edges between v and w minus expectednumber of edges between them is A_(vw)−(k_(v)k_(w))/2m.

$Q = {\frac{1}{2m}{\sum\limits_{vw}{\left\lbrack {A_{vw} - \frac{k_{v}*k_{w}}{2m}} \right\rbrack\frac{{s_{v}s_{w}} + 1}{2}}}}$

The equation above holds for partitioning into two communities only.Hierarchical partitioning (i.e. partitioning into two communities, thenthe two sub-communities further partitioned into two smaller subcommunities only to maximize Q) is a possible approach to identifymultiple communities in a network. The above equation can be generalizedfor partitioning a network into c communities.

$Q = {{\sum\limits_{vw}{\left\lbrack {\frac{A_{vw}}{2m} - \frac{k_{v}*k_{w}}{\left( {2m} \right)\left( {2m} \right)}} \right\rbrack{\delta\left( {c_{v},c_{w}} \right)}}} = {\sum\limits_{i = 1}^{c}\left( {e_{ii} - a_{i}^{2}} \right)}}$

e_(ij) is the fraction of edges with one end vertices in community i andthe other in community j:

$e_{ij} = {\sum\limits_{vw}{\frac{Avw}{2m}1_{v \in c_{i}}1_{v \in c_{j}}}}$

a_(i) is the fraction of ends of edges that are attached to vertices incommunity i:

$a_{i} = {\frac{k_{i}}{2m} = {\sum\limits_{j}e_{ij}}}$

The second scoring function, the Q_Partition score, may be an extensionof the first scoring function Q to be defined on partitions of the spaceS. If the scoring function Q is defined on subsets of S, it can beextended to a partition function Q_Partition in various ways. One of thesimplest ways to extend function Q to partitions is by definingQ_Partition (P) as the sum over p in P of Q(p) (e.g., for a partition P,Q_Partition (P)=sum_{subsets p in P} Q(p)).

In some embodiments, Q_Partition must have the following property: Let Pbe an arbitrary partition of a subset of S, let p belong to P, and let qbe a partition of p. P(q) is defined to be the partition of obtained byreplacing p in P with the elements of q. Then, in this example,Q_Partition must have the following property for all P, p, q asdescribed above:

(1) QP(P(q))>=QP(P) if and only if QP(q)>=Q({p})

In some embodiments, function Q does not need to come from a setfunction in this case. Functions Q_Partition which satisfy property (1)are, by definition, stable partition functions. A class of suchfunctions is described as follows.

Let Q be any real-valued function defined on the set of non-emptysubsets of S. Let A(p,q) be any function defined on pairs of non-emptysubsets such that p is a subset of q. If:

(2) A(p,p)==1 and A(p,q)*A(q,r)=A(p,r), for all legal p,q,r

then we may extend the set function Q( ) to all partitions P by:

(3) QP(P)=sum A(p,U(P))Q(p)

p in P

Note that all real numbers k, A(p,q)==(|p|/|q|){circumflex over ( )}ksatisfies this property. Moreover, k==0 implies A(p,q)==1.

(1) holds for Q defined in (3). If QP and QP′ are stable partitionfunctions, then so is x*QP+y*QP′ for x, y>=0. We also refer to stablepartition functions on S as “partition scoring functions” for F.

For any scoring function of the form (3), a monotonically increasingfunction f may be chosen from the real numbers to itself and replace Qby Q′( )=f(Q( )). In particular, if f( ) is ‘sufficiently invertible’(e.g., A( ) and Q( ) are >=0 and f( ) is invertible on the non-negativereals). QP(P) may be defined by:

(3′) QP′(P)=f-inverse(sum A(p,U(P))f(Q(p)))

p in P

Since f(QP(P)) satisfies (1) and f( ) is monotonically increasing, theQP′ in (3′) also satisfies (1) and extends Q( ) on subsets of S.Concretely, if A==1 and Q(>=0 on sets, QP(P) may be defined to be theEuclidean norm of Q( ) on the individual elements of P, and still get ascoring function. Also can use the exponential function for f( ) withoutrequiring Q to be non-negative.

In various embodiments, there may be extreme values under comparisons,using either <= or >=, for a function Q defined on partitions of subsetsof S. Since Q may be replaced by −Q if the comparison is <=, it may beassumed without loss of generality that maximal values for Q (i.e., >=)are of interest. Specifically, a method for finding the F-subordinatepartition on which Q is maximal, provided Q satisfies a simple property,is disclosed herein.

Given a scoring function Q_Partition on F, we can define a scoringfunction Q_max ( ) to be Q(p) if p is a leaf, and max(Q(p),Qmax(C(p)))if not. One consequence of this definition and requirement (1) onQ_Partition is that the maximal partition of a subset p (that is, thepartition V of p for which Qmax(V) is maximal) is either p or the unionof the maximal partitions of each element of C(p) (ties may be broken bytaking the subset p instead the children).

In various embodiments, the autogrouping method uses a hierarchicalclustering process on S to compute F (i.e., to construct the forest F)and if Q_Partition is a scoring function on the roots R of F, we canfind the Q_Max maximal partition of S subordinate to F. Joining ascoring function Q( ) with hierarchical clustering may provide aprincipled method for choosing among the partitions for the “Q-maximalpartition.”

The partition generation module 2104 may begin with the original space Sand may form a forest F described above. In some embodiments, thegeneration module 2104 takes a partition P and returns a new partitionP′ which is coarser than P. Note that Clumper({S})={S}. Any partition Psuch that generation module 2104 Clumper(P)=P is calledclumper-terminal, and repeated applications must eventually reach aclumper-terminal partition. The sequence Singletons(S),Clumper(Singletons(S)), Clumper(Clumper(Singletons(S))), etc., mayterminate in a finite number of steps, and the union of all thesepartitions forms an atomic forest F whose roots are the elements in aC-terminal partition R, which are the roots of F.

One example process utilizing the scoring functions and generatingpartitions is as follows in the following pseudocode:

 P = INITIAL(S) // some initial partition - often Singletons( ), but itcan  be any partition  F = Tree(P) // node for every subset, rememberconnections, and have  max slot     // to hold partition of the node'sset which has maximal score  for (x in S) { {x}.max = {x} }  BEGIN  P′ =clumper(P)  if P==P′   then   quit   else   UPDATE_Qmax(P′,P)  ENDUPDATE_Qmax(P′,P)  for (p in P′) {   if (!(p in P)) {    Subset pSubset= AddSubset(p,F);    if (Q_Subset(p) >= QP(C(p)))    pSubset.maxPartition = p     pSubset.Qmax = Q(p)    else    pSubset.Qmax = QP(C(p))     pSubset.maxPartition = MAX_UNION(C(p))  }  }  MAX_UNION({Ni})  return the union of Ni.max

When this process terminates, the elements of the roots R of F maycontain their maximal partitions, the union of which is the bestpartition in F of S.

The partition selection module 2112 may find a partition subordinate tothe forest F that maximizes at least one scoring function. For example,the partition selection module 2112 may select a partition subordinateto the forest F that maximizes the scoring function QP.

In various embodiments, each subset of a partition (as discussed herein)may be associated with its own scores. For example, each subset of apartition may be associated with a different Q_Max score. The partitionselection module 2112 may select subsets of unique elements from anynumber of different partitions of the forest F using the Q_Max score togenerate and select a partition.

For example, looking to FIG. 19D, the partition selection module 2112may select subset {A,B,C} from one partition and subsets {D,E}, {F}, AND{G} from another partition based on a scoring function. The selectedsubsets may then form (e.g., generate) a new selected partition P1(e.g., a partition including subsets {A,B,C}, {D,E}, {F}, AND {G}). Theselected partition P1 may be termed an output partition. In thisexample, the partition selection module 2112 may select the subset{A,B,C} from the first partition utilizing the Q_Max score. In a furtherexample, each subset of all partitions that include any of elements A,B, or C, may be associated with a separate Q_Max score. The maximumQ_Max score of all the sets that include any of the elements of A, B, orC is the subset {A,B,C}. As a result, the partition selection module2112 selects that subset {A,B,C} in this example.

Similarly, each subset of all partitions that include any of elements D,E, F, or G, may be associated with a separate Q_Max score. The maximumQ_Max scores of all the sets that include any of the elements of D, E,F, or G are the subsets {D,E}, {F}, and {G} (i.e., the Q_Max scoresassociated with subsets {D, E, F, G}, {D, E, F}, and {G} are not themaximum when compared to the Q_Max scores of subsets {D,E}, {F}, and{G}). As a result, the partition selection module 2112 selects subsets{D,E}, {F}, and {G} in this example.

One example of a scoring function mentioned herein includes a modularityscore for weighted graphs on a node set S. In some embodiments, themodularity score of a subset of a graph proportion of edges within asubset, the e's, and the a's which are the proportion of edges whichcross the boundaries of the subset. The final score may be:e−a{circumflex over ( )}2. In various embodiments, the partitionselection module 2112 selects and/or generates a partition by maximizingthis score. The modularity partition scorer, QP, may be the sum of themodularity scores on the subsets within that partition.

Another example of a scoring function is a variant of entropy for a setS which has an associated classification: that is, a function cls: S→{1,2, . . . , k} (i.e. you have a set and everything has some finitelabel.) For s subset of S, we define p_i(s)=I{x in s:cls(x)==i}|/|s|,provided |s|!=0. Then Q(s)=sum_{classes i} (p_i(s)*log(p_i(s))). Theextension of the entropy scorer Q to a partition scorer, QP is given bythe extension property (3) where A(p,q)=|p|/|q|. In other words, for apartition P, QP(P)=sum_{p in P} (Q(p)*|p|/|U(P)|). Normally one wants tominimize the entropy and the subset scorer here is the negative of thetraditional entropy score by maximizing the scoring function.

The data control module 2114 is configured to provide the selectedand/or generated partition from the partition selection module 2112. Invarious embodiments, the data control module 2114 generates a reportindicating the selected and/or generated partition from the partitionselection module 2112. The report may include, for example, data sets,partitions, subsets, elements, data set identifiers, partitionidentifiers, subset identifiers, element identifiers, and/or the like.In some embodiments, the report may include a graph (e.g., see FIG. 19)with an indication of selected nodes whose member(s) include data of theselected and/or generated partition from the partition selection module2112.

FIG. 22 is an example flowchart for autogrouping in some embodiments. Inthis example, the autogroup module 2002 receives a set S={A, B, C, D, E,F, G} and performs autogrouping to identify a selected partition of aforest based on S. Elements of set S may be, for example, nodes of agraph wherein the graph may be visualized (e.g., a visualization asdiscussed herein) or not visualized. The graph may be a topological dataanalysis graph of nodes and edges as described herein. In someembodiments, the graph may be any network graph that includes nodes,neighborhoods, groupings, communities, and/or data points.

Non-limiting examples describing at least some of the steps in FIG. 22will be described using the graph depicted in FIG. 23. The embodiment ofthe Q_Partition in this example is simply the sum over the subsets ofthe partition P of the Q_Subset scores on each subset. For example, ifP={{A, B, C}, {D}, {E, F}, {G} }, then Q_Partition(P)=Q_Subset({A, B,C})+Q_Subset({D})+Q_Subset({E, F})+Q_Subset({G}).

In step 2202, the data structure module 2102 receives the set S and thepartition generation module 2104 generates an initial partition whichare the singletons of the set S={A, B, C, D, E, F, G}, namely, P_0={{A},{B}, {C}, {D}, {E}, {F}, {G}}. This is illustrated in FIG. 23 as thebottom row (2302) of the depicted forest.

In step 2204, the Q_subset score module 2106 computes the Q_Subset scoreon each subset of the partition P_0. In this example, the Q_subset scoremodule 2106 scores each singleton subset with a value of 0.5. This scoreis shown in FIG. 23 for each subset of partition 2302 as Q_Sub=0.5. Thescoring function in this example, may be a modularity scoring functiondiscussed herein.

In step 2206, the Q_partition score module 2110 computes the maximalpartition of each subset a of P_0 from the children of the subset a inthe constructed forest. Since the subsets a in P_0 have no children inthe forest, the maximal partition of the children of the subset a isitself. Namely, for each subset a in P_0, MaximalPartitionChildren(a)=a.

In this example, the Q_partition score module 2110 computes the maximalpartition of each subset as itself. This is shown in FIG. 23 for eachsubset of partition 2302 as MaxP={A} for subset {A}, MaxP={C} for subset{C}, MaxP={D} for subset {D}, MaxP={E} for subset {E}, MaxP={F} forsubset {F}, and MaxP={G} for subset {G}.

In step 2208, the Q_max score module 2108 computes Q_Max on each subsetof P_0. Recall that since the subsets in P_0 do not have any children,for each subset a in P_0,

$\begin{matrix}{{{Q\_ Max}(a)} = {\max\left( {{{Q\_ Subset}(a)},{{Q\_ Partition}\left( {{MaximalPartitionChildren}(a)} \right)}} \right.}} \\{= {\max\left( {{{Q\_ Subset}(a)},{{Q\_ Partition}(a)}} \right)}} \\{= {{\max\left( {{{Q\_ Subset}(a)},{{Q\_ Subset}(a)}} \right)} = {{Q\_ Subset}(a)}}} \\{= 0.5}\end{matrix}$

In this example, the Q_max score module 2108 scores each subset with avalue of 0.5. This Q_Max score is shown in FIG. 23 for each subset ofpartition 2302 as Q_Max=0.5.

In step 2210, we optionally record the maximal partition of each subseta in P_0 to be partition of the subset a that generated the Q_Max forthat subset. Thus we record the MaximalPartition(a)=a in this initialpartition.

In step 2212, the data structure module 2102 computes the next partitionP_1 (the row labeled 2304 in FIG. 23). Namely, in this example, the datastructure module 2102 groups subsets {A} and {B} into the subset {A, B}and subsets {D} and {E} into subset {D, E}. The data structure module2102 preserved the subsets {C}, {F}, and {G} from the partition P_0 inthe partition P_1.

It will be appreciated that the next partition P_1 may group subsets ofprevious partition(s) (e.g., partition 2304) in any number of ways. Forexample, the data structure module 2102 may group a predetermined numberof subsets together at random and/or may group two or more subsetstogether based on the elements (e.g., based on the underlying data thatthe elements represent). In one example, the data structure module 2102may group elements together using a distance metric and/or any otherfunctions to define relationships in the underlying data and/or within asimilarity space (e.g., reference space).

In various embodiments, the data structure module 2102 may determinewhether the system ends and/or whether a new partition is to becomputed. It will be appreciated that the data structure module 2102 mayperform the determination based on any number of ways. In someembodiments, the data structure module 2102 determines if the nextgenerated partition is equal to the previous partition. If the twopartitions are equal (e.g., have the same subsets), the method mayterminate, otherwise the method may continue to step 2214.

In some embodiments, the data structure module 2102 terminates themethod after a predetermined number of partitions are generated, if apredetermined number of roots are found, and/or the like. In variousembodiments, the data structure module 2102 may terminate the method ifa predetermined number of subsets are present in a computed partition.In another example, the data structure module 2102 may terminate themethod after a predetermined period of time, a predetermined period ofmemory usage, or based on any threshold (e.g., the threshold beingcalculated based on the amount of data received).

In step 2214, the Q_subset score module 2106 computes the Q_Subset scoreon each subset of the partition P_1. In this example, the Q_subset scoremodule 2106 computes Q_Subset({A, B})=0.5 and Q_Subset({D,E})=2. In oneexample, the Q_subset score module 2106 calculates a modularity scorefor elements A and B for Subset {A,B} and a modularity score forelements D and E for Subset {D,E}. As discussed herein, the modularityscore may be based on the edges of nodes A and B for Q_Subset({A, B})modularity score and based on the edges of nodes D and E forQ_Subset({D, E}) modularity score.

As was discussed in the paragraph above describing step 2204, Q_Subsetof each singleton subset is 0.5 (e.g., the previous Q_Subset score forsingleton subsets in step 2304 remains unchanged from step 2302). Thesescores are associated with each subset and are visualized in the FIG. 23as Q_Sub in 2304.

In step 2216, the Q_partition score module 2110 then computes themaximal partition at the children of each subset of P_1. The maximalpartition of the children of the subsets {C}, {F}, and {G} are again theoriginal singleton subset. The maximal partition of the children {A, B}is the set including the maximal partitions of the children of {A, B},namely {{A}, {B} } as depicted in partition 2304 in FIG. 23. Similarlythe maximal partition of the children of {D, E} is the set {{D}, {E} }as also depicted in partition 2304 in FIG. 23.

In step 2218, the Q_max score module 2108 computes the Q_Max on eachsubset of P_1. Recall Q_Max(a)=max(Q_Subset(a),Q_Partition(MaximalPartitionChildren(a)). For the subset {A, B}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {A,B} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {A,B} \right\} \right)},{{Q\_ Partition}\left( \left\{ {\left\{ A \right\},\left\{ B \right\}} \right\} \right)}} \right)}} \\{= {\max\left( {{.5},{{{Q\_ Subset}\left( \left\{ A \right\} \right)} + {{Q\_ Subset}\left( \left\{ B \right\} \right)}}} \right.}} \\{= {\max\left( {0.5,1} \right)}} \\{= 1}\end{matrix}$

For the subset {D, E}:

$\left. \left. \left. {\begin{matrix}{{{Q\_ Max}\left( \left\{ {D,E} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {D,E} \right\} \right)},{{Q\_ Partition}\left( \left\{ {\left\{ D \right\},} \right. \right.}} \right.}} \\{= {\max\left( {2,{{{Q\_ Subset}\left( \left\{ D \right\} \right)} + {{Q\_ Subset}\left( \left\{ E \right\} \right)}}} \right.}} \\{= {\max\left( {2,1} \right)}} \\{= 2.}\end{matrix}\left\{ E \right\}} \right\} \right) \right)$

As displayed in partition 2304 of FIG. 23, Q_Max of {A,B} is 1 and Q_Maxof {D,E} is 2. The Q_Max of singletons {C}, {F}, and {G} in partition2304 remain consistent with the respective subsets in partition 2302.Namely, the Q_Max of each of {C}, {F}, and {G} is 0.5.

In step 2220, we optionally record the maximal partition of each subseta in P_1 that resulted in the Q_Max score. As seen above and in FIG. 23,MaxPartition({A, B})={{A}, {B} } and MaxPartition({D, E})={D, E}.

Step 2212 is repeated. The data structure module 2102 computes the nextpartition P_2, depicted in FIG. 23 as row (partition) 2306. In variousembodiments, the data structure module 2102 may determine whether thesystem ends and/or whether a new partition is to be computed. It will beappreciated that the data structure module 2102 may perform thedetermination based on any number of ways.

In step 2214, the Q_subset score module 2106 computes the Q_Subset scoreon each subset of the partition P_2. In this example, the Q_subset scoremodule 2106 computes Q_Subset({A, B, C})=2 and Q_Subset({D, E, F})=1.5.Again, Q_Subset({G})=0.5. These scores are recorded with each subset andare visualized in the FIG. 23 in partition 2306.

In step 2216, the Q_partition score module 2110 computes the maximalpartition at the children of each subset of P_2. The maximal partitionof the children{G} is the subset {G}. The maximal partition of thechildren {A, B, C} is the set consisting of the maximal partitions ofthe children of {A, B, C}, namely {MaxPartition({A,B}),MaxPartition({C})={{A}, {B}, {C} }. Similarly the maximal partition ofthe children of {D, E, F} is the set {MaxPartition({D, E}),MaxPartition({F})}={{D, E}, {F}}.

This is shown in FIG. 23 for each subset of partition 2306 asMaxP={A,B,C} for subset {A,B,C}, MaxP={{D,E},{F} } for subset {D,E,F,},and MaxP{G} for subset {G}.

In step 2218, the Q_max score module 2108 computes the Q_Max on eachsubset of P_2. Recall Q_Max(a)=max(Q_Subset(a),Q_Partition(MaximalPartitionChildren(a)). For the subset {A, B, C}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {A,B,C} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {A,B,C} \right\} \right)},} \right.}} \\{{Q\_ Partition}\left( \left\{ {\left\{ A \right\},\left\{ B \right\},\left\{ C \right\}} \right) \right)} \\{= {\max\left( {2,{{{Q\_ Subset}\left( \left\{ A \right\} \right)} + {{Q\_ Subset}\left( \left\{ B \right\} \right)} +}} \right.}} \\\left. {{Q\_ Subset}\left( \left\{ C \right\} \right)} \right) \\{= {\max\left( {2,1.5} \right)}} \\{= 2}\end{matrix}$

For the subset {D, E, F}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {D,E,F} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {D,E,F} \right\} \right)},} \right.}} \\\left. {{Q\_ Partition}\left( \left\{ {\left\{ {D,E} \right\},\left\{ F \right\}} \right\} \right)} \right) \\{= {\max\left( {1.5,{{{Q\_ Subset}\left( \left\{ {D,E} \right\} \right)} + {{Q\_ Subset}\left( \left\{ F \right\} \right)}}} \right.}} \\{= {\max\left( {1.5,2.5} \right)}} \\{= 2.5}\end{matrix}$

As displayed in partition 2306 of FIG. 23, Q_Max of {A,B,C} is 2 andQ_Max of {D,E,F} is 2.5 The Q_Max of singleton{G} in partition 2306remains consistent with the respective subset in partition 2304. Namely,the Q_Max {G} is 0.5.

In step 2220, we optionally record the maximal partition of each subseta in P_2 that resulted in the Q_Max score. As seen above,MaxPartition({A, B, C})={{A, B, C} } and MaxPartition({D, E, F})={{D,E}, {F}}.

Step 2212 is repeated. The data structure module 2102 computes the nextpartition P_3, depicted in FIG. 23 as row (partition) 2308. The datastructure module 2102 may determine whether the system ends and/orwhether a new partition is to be computed.

In step 2214, the Q_subset score module 2106 computes the Q_Subset scoreon each subset of the partition P_3. In this example, the Q_subset scoremodule 2106 computes Q_Subset({A, B, C})=2 and Q_Subset({D, E, F, G})=1.These scores are recorded with each subset and are visualized in FIG. 23in partition 2308.

In step 2216, the Q_partition score module 2110 computes the maximalpartition at the children of each subset of P_3. The maximal partitionof the children {A, B, C} is the set consisting of the maximalpartitions of the children of {A, B, C}, namely {MaxPartition({A,B,C})}={{A, B, C}. Similarly the maximal partition of the children of {D,E, F, G} is the set {MaxPartition({D, E, F}), MaxPartition({G})}={{D,E}, {F}, {G}}.

This is shown in FIG. 23 for each subset of partition 2308 asMaxP={A,B,C} for subset {A,B,C} and MaxP={{D,E},{F},{G}} for subset{D,E,F,G}.

In step 2218, the Q_max score module 2108 computes the Q_Max on eachsubset of P_3. Recall Q_Max(a)=max(Q_Subset(a),Q_Partition(MaximalPartitionChildren(a)). For the subset {A, B, C}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {A,B,C} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {A,B,C} \right\} \right)},} \right.}} \\\left. {{Q\_ Partition}\left( \left\{ {A,B,C} \right\} \right)} \right) \\{= {\max\left( {2,{{Q\_ Subset}\left( \left\{ {A,B,C} \right\} \right)}} \right)}} \\{= 2}\end{matrix}$

For the subset {D, E, F, G}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {D,E,F,G} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {D,E,F,G} \right\} \right)},} \right.}} \\\left. {{Q\_ Partition}\left( \left\{ {\left\{ {D,E} \right\},\left\{ F \right\},\left\{ G \right\}} \right\} \right)} \right) \\{= {\max\left( {1,{{{Q\_ Subset}\left( \left\{ {D,E} \right\} \right)} + {{Q\_ Subset}\left( {\left\{ F \right\} +} \right.}}} \right.}} \\{{Q\_ Subset}\left( \left\{ G \right\} \right)} \\{= {\max\left( {1,3} \right)}} \\{= 3}\end{matrix}$

As displayed in partition 2308 of FIG. 23, Q_Max of {A,B,C} is 2 andQ_Max of {D,E,F,G} is 3.

In step 2220, we optionally record the maximal partition of each subseta in P_3 that resulted in the Q_Max score. As seen above,MaxPartition({A, B, C})={{A, B, C} } and MaxPartition({D, E, F, G})={{D,E}, {F}, {G}}.

Although not depicted in method 2200, the method may continue. Forexample, the partition selection module 2112 may identify and/orgenerate a preferred partition from that maximizes one or more scoringfunctions. In this example, the preferred partition is the MaxPartition.As discussed immediately above, the maximal partition of each subset inP_3 is MaxPartition({A, B, C})={{A, B, C}} and MaxPartition({D, E, F,G})={{D, E}, {F}, {G}}. The partition selection module 2112 may identifyand/or generate the autogrouped partition {{A, B, C}, {{D, E}, {F}, {G}.

The data control module 2114 may provide the identified and/or generatedautogrouped partition in a report and/or identify the autogroupedpartition in data or a graph.

FIG. 24 is an example report 2400 of an autogrouped graph of data pointsthat depicts the grouped data in some embodiments. Subsets 2402, 2404,and 2406 are subsets of data points that, together, make a partition(i.e., the autogrouped generated partition 2408). In variousembodiments, data may be received and nodes generated utilizingembodiments described herein (e.g., see description regarding FIG. 4 or8). The nodes that represent at least some of the received data may beautogrouped into a number of subsets 2402, 2404, and 2406 of anautogroup generated partition 2408. The report 2400 may depict thesubsets including the rows of the underlying data associated and/orwithin each subset as well as all or some of the underlying data 2410for that subset.

For example, the autogroup module 2002 may generate a report that showseach subset of datapoints for an autogroup generated partition. Therows, columns, or other data identifiers may be associated with eachsubset. Further, all or some of the data associated with each subset maybe displayed (e.g., including any independent variables such as dataidentifiers, for example, patient identifiers).

The report may allow groups of nodes (e.g., nodes that are part of asubset of the output partition) to be identified. The identified groupsof nodes may be identified in a visualization by coloring the nodes in agroup a similar color, shape of nodes, a graphical element associatedwith nodes in a group (e.g., a box around nodes in a group), and/or inany number of ways. In some embodiments, the identified groups of nodesallow a user to create queries, analyze, and/or view data associatedwith nodes in a group for insights.

In some embodiments, autogrouping may be utilized on a weighted graph.In this example, the set that will be autogrouping is the set of nodesof a weighted graph G. The idea is to automatically partition the graphinto groups of nodes that are strongly-connected in the graph. Anunweighted graph may be transformed into a weighted graph if there is afunction f on the nodes of the graph. The weight for an edge (a,b)between two nodes a and b in the graph G may be defined to be thedifference between the function values: wt(a,b)=|f(a)−f(b)|. In anotherembodiment, this graph may be a visualization generated from a data setand the function on the nodes may be given by a color scheme on thenodes.

In one example, the input graph G may be generated from connectingpoints to their nearest neighbors, where the metric space is a set of2200 points from 5 Gaussian samples in the Euclidean plane. The graphmay be colored by the Gaussian density. The graph is made into aweighted graph by weighting each edge in G by the difference in theGaussian density function at the edge's endpoints.

The method is applied uses the scoring mechanisms described hereinregarding weighted graphs and the modularity scorer applied to theweighted graph G. The resulting maximal partition may be “color coded”(utilizing greyscale) in FIG. 25.

To elucidate the groups, we look at the corresponding points andassignments in a scatter plot of the points in the Euclidean plane inFIG. 26. As the graph G comes from the geometry in this data set, subtlegeometric features are preserved in this decomposition. In other words,in this example, autogrouping partitioned the graph into regions of thegraph that are strongly connected and have similar function(specifically density) values. This is helpful as the data points withineach group are now very similar to each other drawing statisticalconclusions from each subset is much more likely to be statisticallysignificant.

In one application of this embodiment, the original data may be a dataset that is input into the graph construction (e.g., as discussedregarding FIG. 8), which produces a graph (the graph may be in memory ora visualization). The visualization may be colored by the average valueof a function of interest on the data points as discussed herein. Onesuch coloring might be the outcome of interest for the data set such assurvival of patients, power output in an electric generator, etc. Thecoloring is used to convert the graph (e.g., in memory or visualization)into a weighted graph that may be then autogrouped using one or more ofthe autogrouping embodiments described herein. Various autogroupingalgorithm partitions the graph into subsets that are highly connectedand have similar color values.

The groups may be used to create a new color scheme for the graph foruse in a visualization. They may also be used for automatic statisticalanalysis and report generation. Moreover, this process may be used tostart with the dataset, generate a graph (but not necessarily generate avisualization) (e.g., generate all or part of the graph in memory), andthen report to the user the subsets of the final autogrouped maximalpartition together with statistical calculations on those subsets.

As discussed herein, recall that once a filter is computed, data pointsmay be mapped to a reference space and an open cover is generated inthat reference space (see discussion regarding FIG. 8). The elements inthe open cover may be iterated over, together with clustering, togenerate the nodes in the resulting visualization. In one exampledescribed herein, the open cover may take intervals in the referencespace (or cross-products of intervals in the case of more than onefilter). The following embodiment is a data-driven alternative togenerating the open cover in the reference space.

The set S in this embodiment are the projections of the original datapoints into the reference space (e.g., a function such as a gaussiandensity function is applied on the received data points to project tothe reference space). The autogroup module 2002 may operate on aweighted graph built from this projection of the data into the referencespace. For example, for a fixed positive integer k, construct a graph Gon the set S by connecting each point a in S to every point b in S if bis one of a's k-nearest neighbors and a is one of b's k-nearestneighbors (i.e. they are symmetric k-nearest neighbors of each other).In some testing, k=20 produces good results. The edges of the graph maybe weighted by the distance between the edge's endpoints in the embeddedreference space distance. This autogrouping embodiment may utilize ahierarchical single-linkage clusterer that uses distance between pointsin the reference space. The scorer modules (e.g., modules 2106, 2108,and/or 2110 in FIG. 21) may utilize a modularity score built off of theweighted neighborhood graph G.

The result of this embodiment may be a partition P of the projection ofthe data points in the reference space. Now for a fixed positive integerj, we can expand each subset a of P by adding all the j-nearestneighbors in the reference space of the elements in the subset a. Thenew, expanded subsets may no longer be a partition as some points maynow exist in multiple subsets but this new collection of subsets formsthe open cover of the reference space (see discussion regarding FIG. 8)in the graph construction.

In various embodiments, autogrouping may be used for clustering. Forexample, in the embodiments described with regard to FIG. 8, after acover is generated either in the reference space or in the originalspace, data is clustered on each of the subsets in the open cover toidentify nodes (e.g., see steps 808-812). Autogrouping clustering may bean adaptive alternative to single linkage clustering with a fixeddistance cut-off.

For example, the set S is a set data together with a metric whichdefines a distance between any two points in the set S. In thediscussion regarding FIG. 8, these points may have come from the opencover in the reference space. In the current example, the partitiongeneration module 2104 (see FIG. 21) and one or more of the scoremodules (e.g., the Q_Subset score module 2106, the Q_Max score module2108, and/or the Q_Partition score module 2110) operate on a weightedneighborhood graph built from the data. For a fixed positive integer k,a graph G may be constructed on the set S by connecting each point “a”in S to every point “b” in S if “b” is one of “a's” k-nearest neighborsand “a” is one of “b's” k-nearest neighbors under the given metric (i.e.they are symmetric k-nearest neighbors of each other). In someinstances, k=20 produces good results. The edges of this graph may beweighted by the distance between the edge's endpoints. The partitiongeneration module 2104 for this autogrouping example is a hierarchicalsingle-linkage clusterer that uses the distance between pointsdetermined by the given metric. The one or more of the score modules(e.g., the Q_Subset score module 2106, the Q_Max score module 2108,and/or the Q_Partition score module 2110) uses the modularity scorebuilt off of the weighted neighborhood graph G. The resulting clusteringwould likely have clusters formed at a variety of distance cut-offsinstead of a single fixed distance cut-off for the set S.

In another example, the elements of the set S might have additionalinformation such as an associated classification, that is, for example,a function cls: S→{1, 2, . . . , k} (i.e. there is a set and everythinghas some finite label.) The one or more of the score modules (e.g., theQ_Subset score module 2106, the Q_Max score module 2108, and/or theQ_Partition score module 2110) may score entropy (e.g., one or more ofthe score modules may be an entropy scorer).

One example of an entropy scorer Q(a)=sum_{classes i}(p_i(a)*log(p_i(a))) where p_i(a)=I{x in a:cls(x)==i}|/|a|, provided|a|!=0. The extension of the entropy scorer Q to a partition scorer, QPis given by the extension property (3) where A(p,q)=|p|/|q|. In otherwords, for a partition P, QP(P)=sum_{p in P} (Q(p)*|p|/|U(P)|). Thecombination of the partition generation module 2104 and one or more ofthe score modules (e.g., the Q_Subset score module 2106, the Q_Max scoremodule 2108, and/or the Q_Partition score module 2110) may produce themaximal partition (i.e. clustering) of the elements of the set S thatemphasizes clusters that are very close in distance and have the lowestentropy in class type in the subsets of the partition. In other words,this example embodiment may locate clusters that have the largestproportion of each single class type possible under the constraint ofthe distance metric.

In some embodiments, autogrouping may be used for open cover generationwithout a reference space. For example, in the embodiments describedwith regard to FIG. 8, a filter may be generated, points may be mappedto the reference space, and an open cover may be generated in thatreference space (e.g., see steps 802-808). The elements in the opencover may be iterated over, together with clustering, to identify nodes.In some embodiments, the open cover may be constructed in the referencespace. Various embodiments include a data-driven alternative togenerating the open cover of the original data without the need to havea filter or a reference space.

In one example, the set S is the original data together with a metricwhich defines a distance between any two points in the set S. Both thepartition generation module 2104 and the one or more of the scoremodules (e.g., the Q_Subset score module 2106, the Q_Max score module2108, and/or the Q_Partition score module 2110) may operate on aweighted neighborhood graph built from the data. Specifically, for afixed positive integer k, a graph G on the set S is constructed byconnecting each point “a” in S to every point “b” in S if “b” is one of“a's” k-nearest neighbors and “a” is one of “b's” k-nearest neighborsunder the given metric (i.e. they are symmetric k-nearest neighbors ofeach other). In some instances, k=20 produces good results. The edges ofthis graph may be weighted by the distance between the edge's endpoints.The partition generation module 2104 for this embodiment is ahierarchical single-linkage clusterer that uses the distance betweenpoints determined by the given metric. One or more of the score modules(e.g., the Q_Subset score module 2106, the Q_Max score module 2108,and/or the Q_Partition score module 2110) may use the modularity scorebuilt off of the weighted neighborhood graph G.

The result in this example is a partition P of the data points in theoriginal space. For a fixed positive integer “j”, we can expand eachsubset “a” of P by adding all the j-nearest neighbors of the elements inthe subset “a”. The new, expanded subsets may no longer be a partitionas some points may now exist in multiple subsets but this new collectionof subsets may form the open cover of the space for step 808 asdescribed in FIG. 8.

It will be appreciated that many graphs may be generated for the samedata. For example, TDA may be utilized with different filters, metrics,resolutions, or any combination to generate different graphs (anddifferent visualizations) using the same large data set. Each graph mayprovide different insights based on the data's shape, character, orboth. Different filters, metrics, resolutions, or combinations maygenerate different graphs that include different groupings of nodes. Itmay be advantageous for a data scientist to generate at least one graphthat includes or depicts groupings of nodes that share a similarcharacteristic (e.g., outcome). These graphs with this type of groupings(e.g., if the groupings are coherent relative to other nodes in the samegraph, relative to other nodes in other graphs, or both) may suggestinsights or relationships within the data that were not previouslyavailable through query methods.

In some embodiments, different graphs may be generated on the same datausing TDA methods described herein. Each different graph may begenerated based on different filters, metrics, resolutions, or anycombination. All or some nodes within each graph may be grouped based ona shared characteristic such as outcome. For example, if the outcome issurvival, then nodes may be grouped based on those that survived andthose that did not survive. In some embodiments, nodes are only groupedif they share the same outcome (e.g., they share the same sharedcharacteristic), there is a sufficient number of nodes in the group,there is a sufficient number of connections between nodes betweengroups, or any combination. Each graph may be scored based on thegroupings within that particular group. Each graph may then be orderedbased on score and one or more of the graphs may be identified orprovided to a user based on the ordered score (e.g., visualizations ofthe top three graphs based on best score is displayed or otherwiseprovided to the user).

It will be appreciated that this system of automatic outcome analysismay allow a data scientist to have data assessed through a variety ofdifferent filters, metrics, resolutions, or any combination to find apreferred graph without manual selection of each filter, metric, andresolution. Further, the data scientist may not be required to manuallyinspect each visualization of each graph to identify one or more graphsthat have the best grouping of nodes based on outcome.

In various embodiments, systems and methods described herein may assistin identifying one or more graphs that best localizes an outcome orshared characteristic (e.g., of one or more columns in the data set).For example, a graph that best localizes an outcome may include ordepict regions of the graph that have categories of or similar valuesfrom the outcome concentrated together). In some embodiments, systemsand methods described herein may generate scores to identify metricsthat are “continuous” with respect to the outcome column, identifylenses that have distributions of the outcome categories that are“localized,” and choose lens parameters that are localized and separateoutcome categories (or similar outcome values) without having too manysingletons.

FIG. 27 depicts a visualization 2700 of a graph that illustratesoutcomes that are not significantly localized. The visualization 2700 iscolored in greyscale based on outcome. The nodes of the visualization2700 appear generally connected and black nodes appear to collect onthree sides of the visualization 2700.

FIG. 28 depicts a visualization 2800 of a graph that illustratesoutcomes that are more localized than FIG. 27. FIGS. 27 and 28 maydepict different visualizations of the same data (e.g., TDA, asdiscussed herein, is performed on the same data but different filters,metrics, resolutions, or combinations were used to generate differentgraphs and visualizations) Like FIG. 27, the visualization 2800 iscolored in greyscale based on outcome. It will be appreciated that nodesthat share similar outcome and are similarly colored are more denselypacked and more interconnected than those depicted in FIG. 27. A datascientist may perceive additional insights, identify interrelationshipsin the data, or both using FIG. 28 rather than FIG. 27.

FIG. 29 is a block diagram of an exemplary analysis server 208 includingan autogroup module 2002 and an outcome analysis module 2902. Theexemplary analysis server 208 depicted in FIG. 20 may be similar to theexemplary analysis server 208 depicted in FIGS. 2 and 20. In exemplaryembodiments, the analysis server 208 comprises a processor 302,input/output (I/O) interface 304, a communication network interface 306,a memory system 308, and a storage system 310.

The storage system 310 comprises a plurality of modules utilized byembodiments of the present invention. A module may be hardware (e.g., anASIC), software (e.g., including instructions executable by aprocessor), or a combination of both. In one embodiment, the storagesystem 310 comprises a processing module 312 which comprises an inputmodule 314, a filter module 316, a resolution module 318, an analysismodule 320, a visualization engine 322, a database storage 324, and anautogroup module 2002. Alternative embodiments of the analysis server208 and/or the storage system 310 may comprise more, less, orfunctionally equivalent components and modules.

The input module 314 may be configured to receive commands andpreferences from the user device 202 a. In various examples, the inputmodule 314 receives selections from the user which will be used toperform the analysis. The output of the analysis may be an interactivevisualization.

The input module 314 may provide the user a variety of interface windowsallowing the user to select and access a database, choose fieldsassociated with the database, choose a metric, choose one or morefilters, and identify resolution parameters for the analysis. In oneexample, the input module 314 receives a database identifier andaccesses a large multi-dimensional database. The input module 314 mayscan the database and provide the user with an interface window allowingthe user to identify an ID field. An ID field is an identifier for eachdata point. In one example, the identifier is unique. The same columnname may be present in the table from which filters are selected. Afterthe ID field is selected, the input module 314 may then provide the userwith another interface window to allow the user to choose one or moredata fields from a table of the database.

Although interactive windows may be described herein, it will beappreciated that any window, graphical user interface, and/or commandline may be used to receive or prompt a user or user device 202 a forinformation.

The filter module 316 may subsequently provide the user with aninterface window to allow the user to select a metric to be used inanalysis of the data within the chosen data fields. The filter module316 may also allow the user to select and/or define one or more filters.

The resolution module 318 may allow the user to select a resolution,including filter parameters. In one example, the user enters a number ofintervals and a percentage overlap for a filter.

The analysis module 320 may perform data analysis based on the databaseand the information provided by the user. In various embodiments, theanalysis module 320 performs an algebraic topological analysis toidentify structures and relationships within data and clusters of data.It will be appreciated that the analysis module 320 may use parallelalgorithms or use generalizations of various statistical techniques(e.g., generalizing the bootstrap to zig-zag methods) to increase thesize of data sets that can be processed. The analysis is furtherdiscussed in FIG. 8. It will be appreciated that the analysis module 320is not limited to algebraic topological analysis but may perform anyanalysis.

The visualization engine 322 generates an interactive visualizationincluding the output from the analysis module 320. The interactivevisualization allows the user to see all or part of the analysisgraphically. The interactive visualization also allows the user tointeract with the visualization. For example, the user may selectportions of a graph from within the visualization to see and/or interactwith the underlying data and/or underlying analysis. The user may thenchange the parameters of the analysis (e.g., change the metric,filter(s), or resolution(s)) which allows the user to visually identifyrelationships in the data that may be otherwise undetectable using priormeans. The interactive visualization is further described in FIGS. 9-11.

The database storage 324 is configured to store all or part of thedatabase that is being accessed. In some embodiments, the databasestorage 324 may store saved portions of the database. Further, thedatabase storage 324 may be used to store user preferences, parameters,and analysis output thereby allowing the user to perform many differentfunctions on the database without losing previous work.

The autogroup module 2002 is configured to autogroup data points of adata set or nodes in a graph. As discussed herein, the groupings may beapproximations of possible maxima of a given scoring function thatscores possible partitions of the original data object (e.g., acollection of data points or a collection of nodes of a graph). Theautogroup module 2002 may, in some embodiments, perform autogrouping ofnodes of a graph (whether a visualization is generated or not). Invarious embodiments, the autogroup module 2002 may perform autogroupingfor reference space open cover generation. The autogroup module 2002 mayautogroup any number of data points, sets of data points,representations, and/or the like. The autogroup module 2002 is furtherdiscussed in FIG. 21.

The outcome analysis module 2902 is configured to select metric-lenscombinations, select resolutions, generate graphs using the selectedmetric-lens(es) combinations and resolutions, group nodes bydistribution of outcomes of member data points to score each graph, andselect one or more graphs based on score (e.g., based on similar or sameoutcomes or distribution of outcomes). The outcome analysis module 2902may generate visualizations of the selected graphs. The outcome analysismodule 2902 is further described with regard to FIG. 30.

It will be appreciated that that all or part of the processing module212 may be at the user device 202 a or the database storage server 206.In some embodiments, all or some of the functionality of the processingmodule 312 may be performed by the user device 202 a.

FIG. 30 depicts an example outcome analysis module 3002 in someembodiments. The outcome analysis module 3002 may comprise a metricselection module 3002, a metric-lens combination selection module 3004,a lens parameter selection module 3006, group identification module3008, group score module 3010, a graph score module 3012, a graphselection module 3014, and an outcome visualization module 3016. Themetric selection module 3002 may select one or more metrics for testingfrom a set of metrics. In some embodiments, a user may provide themetric selection module 3002 with one or more metrics to test. Themetric selection module 3002 may test a metric in a number of ways. Inone example, the metric selection module 3002 may receive a data setand, for each data point in the data set, determine one or more closestneighboring data points using the metric to be tested. For each point inthe data set, the metric selection module 3002 may improve (e.g.,increase) a metric score if the one or more closest neighboring datapoints to that data point share a similar or same sharedcharacteristic(s) (e.g., similar or shared outcome). In this way, themetric selection module 3002 may generate a metric score for the graph.The metric selection module 3002 may similarly generate a metric scorefor each metric using the same data set. The metric selection module3002 may select one or more metrics and generate a subset of metricsincluding the one or more selected metrics using the metric scores(e.g., based on highest data scores). The process is further describedherein.

The metric-lens combination selection module 3004 may select one or morelens(es) to combine with one or more metric(s) of the subset of metrics.In various embodiments, the metric-lens combination selection module3004 may select lenses from a set of lenses or receive one or morelenses to test from a user. For each metric-lens(es) combination, themetric-lens combination selection module 3004 may compute a referencemap of the lens space using the data from the originally received dataset and compute a category entropy of each subspace of each referencemap. The metric-lens combination selection module 3004 may compute ametric-lens score based on a sum of category entropies of a particulargraph. The metric-lens combination selection module 3004 may select oneor more metric-lens(es) combinations based on the metric-lens score. Theprocess is further described herein.

The lens parameter selection module 3006 may select lens parameters(e.g., resolution, gain, or both). The choice of resolution may dependon the number of points in the space and the number of lenses. Theprocess is further described with regard to FIG. 38.

The group identification module 3008 identifies groups within a graph(e.g., a topological graph) generated using at least one of the subsetof metric-lens combination and at least one selected resolution. Thegroup identification module 3008 may identify one or more groups thatshare the same or similar outcomes (or distribution of outcomes) usingautogrouping as described herein.

The group score module 3010 may generate a group score for each groupidentified by the group identification module 3008. The group score maybe based in part on entropy of the group. The graph score module 3012generates a graph score based on the entropy of each group within thatgraph. A graph selection module 3014 may select one or more graphs toprovide or identify for the user based, at least in part, on the graphscore. The visualization module 3016 may generate one or morevisualizations of graphs selected by the graph selection module 3014.These processes are further described herein.

FIG. 31 is a flowchart for outcome auto analysis in some embodiments. Instep 3102, the input module 314 may receive data including or from alarge data set. Systems and methods described herein may be utilized inbig data analysis. Big data is a term that refers to data sets that arelarge and/or complex such that traditional data processing of the priorart may be inadequate or limited. Massive data sets may include hundredsof thousands, millions, or even billions (or more) data points and/orany number of characteristics per data point. There may be significanthardware, service, and/or financial limitations that must be consideredwhen attempting to analyze large data sets (e.g., up to an includingmassive data sets as described above). In some embodiments, the data,data set, or both may be large (e.g., massive) data sets.

In various embodiments, the data received in step 3102 may include oneor more shared characteristics in columns related to each row or datapoint. In one example, the one or more shared characteristics mayinclude one or more outcomes. The outcomes may, in some embodiments, becategorical. If the data is medical data, the shared characteristics mayinclude, for example, patient outcomes after treatment(s), services, orthe like. Shared characteristics, such as outcomes, may be continuous ordiscrete. A discrete outcome may individually separate and distinct(e.g., the data may be divided in part on those who survived and thosewho died). Alternately, the outcomes may be measured as part of acontinuum. In some embodiments, the metric selection module 3002 mayperform discretization of continuous features of outcomes from one ormore outcome columns of the data to generate outcome categories that arediscrete.

In various embodiments, the method described herein may search throughexisting metrics and lenses to produce one or more graphs that separatethe outcome groups. The separations may not necessarily be based ongeometry. One existing example method includes steps for choosing orselecting one or more metrics, choose or select one or more lenses, andchoose or select one or more scale parameters. Those metrics that arechosen or selected may make the outcome (e.g., shared characteristic(s))column(s) “continuous” with respect to the metric. Those lenses (e.g.,metric-lens(es) combination(s)) that are chosen or selected may assistin localizing outcome values. Those scale parameters that are chosen orselected may both localize and separate different outcome values withoutmaking too many singeltons (e.g., nodes of a graph of visualization witha single data point as a member or few data points as a member). Theseprocesses are further described below.

The data may be related to any science, profession, service, or the like(e.g. the data may be medical data, financial data, scientific data,energy data, or the like).

In step 3104, the metric selection module 3002 selects a subset ofmetrics. The subset may include any number of metrics from a set ofmetrics or pseudo-metrics (e.g., does not satisfy the triangleinequality) from a set of metrics. Metrics that are selected may be usedwith received data to generate “continuous” results with respect to theshared characteristics (e.g., outcome column(s)). For example, pointsthat are similar in the metric may have similar or the same outcomecharacteristics (e.g., outcome categories).

In various embodiments, the metric selection module 3002 buildsneighborhoods for each point. For each point, the metric selectionmodule 3002 may count the number of points whose nearest neighbor hasthe same shared characteristic category as the particular point togenerate a metric score for each metric. Different metric scores may becompared to select those metrics that are “continuous” with respect tothe shared characteristics. The process is further described with regardto FIG. 32.

In step 3106, the metric-lens combination selection module 3002 selectsa subset of metric-lens(es) combinations based in part on the subset ofmetrics. In various embodiments, metric-lens(es) combinations may beselected to test (e.g., any number of metric-lens(es) combinations maybe tested). The metric-lens combination selection module 3002 may selectthe metric-lens(es) combinations that localize outcome values (e.g.,relative to other metric-lens(es) combinations. The process is furtherdescribed with regard to FIG. 35.

In step 3108, the lens parameter selection module 3006 may select aresolution. In various embodiments, the lens parameter selection module3006 chooses resolution and gain. The choice of resolution may depend onthe number of points in the space and the number of lenses. The processis further described with regard to FIG. 38.

In step 3110, the processing module 212 generates a topological graphusing at least some of the metric-lens(es) combinations from the subsetof metric-lens combinations and at least one of the resolutions fromsubset of resolutions. The process is further described with regard toFIG. 38, step 3802 of which is analogous to step 3110.

In step 3112, the group identification module 3008 characterizes nodesof each group based on outcome(s) (e.g., shared characteristic(s)). Invarious embodiments, the group identification module 3008 colors orotherwise denotes each node of the topological graph based on outcome(s)of member points of that node or distribution of outcome(s) of memberpoints of that node. The process is further described with regard toFIG. 38, step 3804 of which is analogous to step 3112.

In step 3114, the group identification module 3008, the autogroup module2002, or both identify groups of nodes that share the same or similaroutcome of each graph. This process may utilize autogrouping describedherein to determine edge weight between nodes, identify a partition thatincludes groupings of nodes based on distribution of outcome(s) of datapoints, or both. The process is further described with regard to FIG.38, step 3806 of which is analogous to step 3114.

In step 3116, the group score module 3010 scores each graph based onidentified groups. In various embodiments, the group score module 3010may score each group in a graph based on entropy of groups, number ofdata points, number of nodes in a group, number of outcome categories(e.g., discrete outcome groups of the outcomes identified using theoriginally received data), or any combination. The graph score module3012 may generate a graph score for each graph based on the group scoresof that graph. These processes are further described with regard to FIG.38, step 3810 of which is analogous to 3116.

In step 3118, the graph selection module 3014 may select one or moregraphs based on the graph scores. The graph selection module 3014 mayselect a graph in any number of ways. For example, the graph selectionmodule 3014 may select one or more graphs based on the highest scores,may select the top graph based on score, may select all graphs withgraph scores above a predetermined threshold, may select a predeterminednumber of graphs in order of the highest score, or may not select anygraph below a predetermined threshold. The process is further describedwith regard to FIG. 38, step 3812 of which is analogous to step 3118.

In step 3120, the graph visualization module 3016 generatesvisualizations for each selected graph and provides the visualizationsto a user. The graph visualization module 3016 may provide thevisualizations in any order including, for example, beginning with avisualization of the graph with the highest graph score. In someembodiments, for each graph, the graph visualization module 3016displays information related to generating the particular graph,including for example metric information indicating the metric used,metric-lens information indicating the lens(es) used, resolutioninformation indicating the resolution and gain used, graph score, or anycombination. The process is further described with regard to FIG. 38,step 3814 of which is analogous to step 3120.

FIG. 32 is a flowchart for selection of a subset of metrics in someembodiments. In various embodiments, the metric selection module 3002may receive or retrieve a set of metrics. The metrics may be of anytype. Example metrics include, but are not limited to VarianceNormalized Euclidean (“VNE”), Euclidean, Manhattan, Chebyshev,Interquartile Range Normalized Euclidean, Angle, Cosine, PearsonCorrelation, Absolute Correlation, or the like. The set of metrics maybe a subset of another set of metrics. For example, metrics may beinitially chosen based on the type of data received from the originaldata set. Metrics may be initially chosen in any number of ways oralternately, a set of metrics may be tested as described with regard toFIG. 32 regardless of the underlying data.

In step 3202, for each metric, the metric selection module 3002 usesthat particular metric to identify one or more of the closest points forthe points in the data set. The metric selection module 3002 may notbuild a graph. For example, assume metrics VNE, Euclidean, and Manhattanare to be tested using the method described regarding FIG. 32. For eachpoint in the data set, the metric selection module 3002 may identify oneor more closest points for each metric. For example, for each point inthe data set, the metric selection module 3002 may identify the closestpoints within the data set using the first metric (e.g., VNE) to createa first metric dataset. The process may be repeated for the othermetrics. For example, for each point in the data set, the metricselection module 3002 may identify the closest points within the dataset using the second metric (e.g., Euclidean) to create a second metricdataset. As follows, for each point in the data set, the metricselection module 3002 may identify the closest points within the dataset using the third (e.g., Manhattan) to create a third metric dataset.

In step 3204, for each point in each graph, the metric selection module3002 calculates a point score for that graph based on the nearestneighbor to that point which as the same or similar outcome (e.g., sameor similar shared characteristic) to that particular point. In someembodiments, for each point in the first graph, the metric selectionmodule 3002 may find the nearest point. If the nearest point shares thesame or similar outcome as the particular point, then the metricselection module 3002 may add a value to a point score or otherwisechange the value of a point score. If the nearest point does not sharethe same or similar outcome to the particular point, the metricselection module 3002 may not add a value to a point score, may notchange the value of the point score, or change the value of a pointscore to indicate the dissimilarity in the point score for the graph.

For example, after the metric selection module 3002 generates a graphusing data and a first metric, the metric selection module 3002 mayselect a particular point in the graph. The metric selection module 3002may then identify the closest point to the particular point in the graphand determine if the two points have the same or similar outcome. Ifthey have the same outcome, the metric selection module 3002 may changethe value of the point score for the graph. If they do not have the sameoutcome, the metric selection module 3002 may not change the value ofthe point score. The metric selection module 3002 will repeat thisprocess for each point in the graph (e.g., determining that point'snearest neighboring point, determining if the two points have the sameor similar outcome, and changing the value of the point score if theyhave similar values).

The metric selection module 3002 may repeat this process for each graph.It will be appreciated that each graph may be associated with a pointscore based on the point scores of points and neighboring points in thatparticular graph. It will be appreciated that the metric selectionmodule 3002 may generate metric scores using nearest neighboring pointsand shared outcomes in any number of ways.

In some embodiments, for each point of a graph, the metric selectionmodule 3002 may identify any number of nearest neighboring points. Themetric selection module 3002 may generate or change the metric scorebased on the outcomes associated with the nearest neighboring points(e.g., an average outcome of the nearest neighboring points, a majorityof the nearest neighboring points, or the like).

In step 3206, the metric selection module 3002 may calculate metricscores for each graph using the point scores. In some embodiments, themetric scores are calculated when a point score is changed, a pointscore is added to the metric score, or a point score is subtracted fromthe metric score in step 3204. The point score for any number of pointsmay change the metric score in any number of ways. The point score maybe the metric score.

In step 3208, the metric selection module 3002 may optionally rank orcompare each graph for each metric using the metric scores. For example,each graph may be associated with a different metric score and thegraphs may be ordered or ranked from highest metric score to lowestmetric score.

In step 3210, the metric selection module 3002 selects one or moremetrics using the metric scores. For example, the metric selectionmodule 3002 may identify the graph associated with a metric as havingthe highest metric score and the metric selection module 3002 may selectthe associated metric as a member of the selected subset of metrics. Themetric selection module 3002 may select a percentage of the metricscores indicating consistent more outcomes of neighboring points (e.g.,the metric selection module 3002 may select metrics based on the top 20%of metric scores) or a predetermined number of metrics using the metricscores (e.g., the metric selection module 3002 selects four metricsassociated with the top four graphs based on highest metric scores). Itwill be appreciated that the metric selection module 3002 may selectmetrics using the metric scores in any number of ways.

FIGS. 33A-33C depict example graphs including the same data points fromGaussian data and different metrics. Although only metrics for VNE,Manhattan, and Absolute Correlation are shown, it will be appreciatedthat any number of different metrics may be used to generate graphs totest the metrics. FIG. 33A depicts an example graph using a VNE metric.Each data point may be colored or otherwise associated with an outcome(e.g., from an outcome column of the data). FIG. 33A depicts groupingsof data points with fairly consistent outcomes. This may represent adesirable graph. Metric score 3302 is high compared to the metric score3306 of FIG. 33C. The metric score 3302 may be based on point scores foreach point in the graph (e.g., each point in the graph contributing apoint score to the metric score 3302 if the nearest neighbor to thatpoint shares the same or similar outcome).

FIG. 33B depicts an example graph using a Manhattan metric. Like FIG.33A, FIG. 33B depicts groupings of data points with fairly consistentoutcomes. This may also represent a desirable graph. Metric score 3304is the same as metric score 3302 of FIG. 33A but is also high comparedto the metric score 3306 of FIG. 33C.

FIG. 33C depicts an example graph using an Absolute Correlation metric.FIG. 33C depicts one large group with different outcomes that areintermixed. The metric score 3306 is low compared to metric scores 3302and 3304.

In some embodiments, based on the graphs FIG. 33A-C, the metric scores3302, 3304, and 3306, or both, the metric selection module 3002 mayselect metrics VNE and Manhattan for the subset of metrics. The metricselection module 3002 may not select the Absolute Correlation for thesubset of metrics because the points, colored or otherwise identified byoutcome, are intermixed (e.g., relative to other graphs).

FIGS. 34A-34C depict example graphs including the same data points fromMNIST data and different metrics. FIG. 34A depicts an example graphusing an Angle metric. Each data point may be colored or otherwiseassociated with an outcome (e.g., from an outcome column of the data).FIG. 34A depicts groupings of data points with fairly consistentoutcomes although there are some relatively minor intermixed data. Thismay represent a desirable graph. Metric score 3402 is high compared tothe metric score 3406 of FIG. 34C and metric score 3408 of FIG. 34D. Themetric score 3402 may be based on point scores for each point in thegraph (e.g., each point in the graph contributing a point score to themetric score 3402 if the nearest neighbor to that point shares the sameor similar outcome).

FIG. 34B depicts an example graph using a Euclidean metric. Like FIG.34A, FIG. 34B depicts groupings of data points with fairly consistentoutcomes with some intermixing. This may also represent a desirablegraph. Metric score 3404 is similar albeit a little lower than metricscore 3402 of FIG. 34A but is also high compared to the metric score3406 of FIG. 34C and metric score 3408 of FIG. 34D.

FIG. 34C depicts an example graph using a Norm. Correlation metric. FIG.34C depicts a generally large shape but with somewhat consistentgroupings of nodes that share outcomes. There are more intermixed nodeswith different outcomes when compared to FIGS. 34A and 34B, however themetric score 3406 is still reasonably close to metric score 3402 of FIG.34A and metric score 3404 of FIG. 34B. The metric score 3406 is highcompared to metric score 3408 of FIG. 34D.

FIG. 34D depicts an example graph using a Chebyshev metric. FIG. 34Ddepicts a fairly large group with different outcomes that are moreintermixed. The metric score 3408 is low compared to metric scores 3302of FIG. 34A, metric score 3304 of FIG. 34B, and metric score 3306 ofFIG. 34C.

In some embodiments, based on the graphs depicted in FIGS. 34A-34D, themetric scores 3402, 3404, 3406, and 3408, or both, the metric selectionmodule 3002 may select metrics Angle, Euclidean, and potentially Norm.Correlation. In some embodiments, the metric selection module 3002 maybe configured to only select the top 2 metrics or any limited number ofmetrics. In that case, the metric selection module 3002 may only selectthe Angle metric and the Euclidean metric. The metric selection module3002 may not select the Chebyshev metric for the subset of metricsbecause the points, colored or otherwise identified by outcome, areintermixed (e.g., relative to other graphs).

FIG. 35 is a flowchart of selection of a subset of metric-lenscombinations in some embodiments. In various embodiments, themetric-lens combination selection module 3004 identifies one or morepotential lenses for each metric of the subset of metrics for testing.The metric-lens combination selection module 3004 may also test andscore each metric in combination with one or more lenses to evaluate ametric-lens combination. It will be appreciated that the metric-lenscombination selection module 3004 may select any different combinationof metric and lens(es). For example, the metric-lens combinationselection module 3004 may identify the same metric but with differentlens combinations for testing.

In step 3502, for each metric of the subset of metrics, the metric-lenscombination selection module 3004 identifies one or more lenses toevaluate in association with the metric. In various embodiments, themetric-lens combination selection module 3004 may identify any number oflenses that may be associated with a metric of the subset of metrics. Itwill be appreciated that some metric-lens combinations may not functionor otherwise may not be desirable without further assessment. Themetric-lens combination selection module 3004 may filter knownnonfunctional or undesirable lenses before selecting metric-lenscombinations. In some embodiments, the metric-lens combination selectionmodule 3004 may combine known functional metric-lens combinations orpotentially desirable metric-lens(es) combination for furtherevaluation. The metric-lens combination selection module 3004 maygenerate a set of metric-lens combinations.

In various embodiments, the metric-lens combination selection module3004 may receive one or more lenses to combine with a metric and includein the subset of metric-lens combinations. For example, a user may inputa preferred or desired lens to test with a metric of the subset ofmetrics. The metric-lens combination selection module 3004 may evaluatethe provided lens in conjunction with one or more metrics or,alternately, the metric-lens combination selection module 3004 mayinclude the provided lens in the subset of metric-lens combination.

In step 3504, for each metric-lens(es) combination, the metric-lenscombination selection module 3004 computes a reference map of the lensspace using data from the originally received data set (e.g., all orpart of the data received in step 3102 of FIG. 31). The reference map isgenerated using both the metric and lens(es) of the metric-lens(es)combination. In various embodiments, the metric-lens combinationselection module 3004 generates a separate reference map for eachmetric-lens(es) combination.

In step 3506, the metric-lens combination selection module 3004 computescategory entropy for each subspace of each graph (e.g., each referencemap). Category entropy may be, for example, entropy of outcome(s) (e.g.,entropy of shared characteristic(s)). In various embodiments, themetric-lens combination selection module 3004 may divide a graph intosubspaces. For example, the metric-lens combination selection module3004 may divide the graph into subspaces of equal or variable size witheach subspace being adjacent to other subspaces. In some embodiments,the metric-lens combination selection module 3004 may divide groupingsof data points into separate subspaces. The metric-lens combinationselection module 3004 may compute entropy for one or more subspacesbased on shared characteristic(s) such as outcome(s) of the data pointsin the subspace(s).

In step 3508, the metric-lens combination selection module 3004 computesa metric-lens score of the metric-lens(es) combination by addingcategory entropy across all subspaces of a graph (e.g., a referencemap). The metric-lens combination selection module 3004 may also computethe metric-lens score based, at least in part, on the number of pointswithin each subspace. The metric-lens combination selection module 3004may generate a metric-lens score for each reference map generated instep 3504. For example, for each graph, the metric-lens combinationselection module 3004 may generate a metric-lens score based on thecategory entropy across any number of subspaces of that particular graphand based on the number of points within any number of subspaces. Themetric-lens combination selection module 3004 may optionally rank (e.g.,order) the graphs based on metric lens scores (e.g., from highest tolowest or in any other method).

In step 3510, the metric-lens combination selection module 3004 selectsa subset of metric-lens(es) combinations based on the metric lens score.In various embodiments, the metric-lens combination selection module3004 may be configured to choose a predetermined number ofmetric-lens(es) combinations, choose metric-lens(es) combinations with ametric-lens score that meet a certain criteria (e.g., choosemetric-lens(es) combinations with a metric-lens score over apredetermined threshold), choose metric-lens(es) that are in a toppredetermined percentage, or any other method.

FIGS. 36A-36C depict example graphs including the same data points fromGaussian data and different metric-lens(es) combination. Althoughmetrics for Euclidean (L2), Angle, and Manhattan (L1) are shown, it willbe appreciated that any number of different metric-lens(es) combinationsmay be used to generate graphs to test the metric-lens(es) combinations.For example, Euclidean (L2) and neighborhood lens (e.g., neighborhoodlens 1, neighborhood lens 2, or both) may be used. In another example,Chevyshev (L-Infinity) and neighborhood lens 1, neighborhood lens 2,multidimensional scaling (MDS) coordinates 1, MDS coordinates 2, metricPrincipal Component Analysis (PCA) coordinates 1, metric PCA coordinates2, or any combination may be used. In a further example, variancenormalized Euclidean metric and neighborhood lens (e.g., neighborhoodlens 1, neighborhood lens 2, or both) may be used. Other examplesinclude a cosine metric with PCA coord. 1, PCA coord. 2, L1 Centrality,Gaussian density, MDS coord. 1, MDS coord. 2, or any combination. Anyother metric, lens(es), or combination may be used.

FIG. 36A depicts an example graph using a Euclidean (L2) metric with aneighborhood lens. Each data point may be colored or otherwiseassociated with an outcome (e.g., from an outcome column of the data).FIG. 36A depicts groupings of data points with fairly consistentoutcomes with limited entropy. This may represent a desirable graph witha higher metric-lens score when compared to metric-lens scoresassociated with graphs of FIGS. 36B and 36C.

FIG. 36B depicts an example graph using an angle metric and MDS Coord. 2lens. FIG. 36B has greater entropy for the graph (e.g., when entropy isadded across subspaces of the graph), the entropy for the graph in FIG.36B may be greater than the entropy for the graph of FIG. 36A.

FIG. 36C depicts an example graph using a Euclidean (L1) using aneighborhood lens. FIG. 36C depicts a line with that may have lowerentropy when compared to the entropy of FIG. 36B.

In some embodiments, based on the graphs FIGS. 36A-36C, the metric-lensscores of the graphs, or both, the metric-lens combination selectionmodule 3004 may select the Euclidean (L2) and neighborhood lens for thesubset of metric-lens(es). The metric-lens combination selection module3004 may select the Euclidean (L1) and neighborhood lens for the subsetof metric-lens(es) as well. The metric-lens combination selection module3004 may not select the Angle metric with the MDS Coor. 2 lens based ona comparison of the entropy of the graph to entropy of graphs depictedin FIGS. 36A and 36B.

FIGS. 37A-37C depict example graphs including the same data points fromMNIST data and different metric-lens(es) combinations. FIG. 37A depictsan example graph using a Cosine metric and neighborhood lens. Each datapoint may be colored or otherwise associated with an outcome (e.g., froman outcome column of the data). FIG. 37A depicts groupings of datapoints with some mixed outcomes. The entropy associated with the graphin FIG. 37A, and therefore the metric-lens score associated with thegraph, may be lower than the entropy and/or metric-lens scores of thegraphs depicted in FIGS. 37B and 37C.

FIG. 37B depicts an example graph using a Euclidean metric andneighborhood lens. FIG. 37B is similar to that in FIG. 37A and may havesimilar or greater entropy for the graph (e.g., when entropy is addedacross subspaces of the graph) than the entropy for the graph of FIG.37A.

FIG. 37C depicts an example graph using a Cosine using a PCA lens. FIG.37C depicts a mixture of outcomes and a higher entropy when compared tothe entropy of FIGS. 37A and 37B.

In some embodiments, based on the graphs FIG. 37A-C, the metric-lensscores of the graphs, or both, the metric-lens combination selectionmodule 3004 may select the Cosine and neighborhood lens for the subsetof metric-lens(es). The metric-lens combination selection module 3004may select the Euclidean (L1) and neighborhood lens for the subset ofmetric-lens(es) as well. The metric-lens combination selection module3004 may not select the Cosine with the PCA lens based on a comparisonof the entropy of the graph to entropy of graphs depicted in FIGS. 37Aand 37B.

FIG. 38 is a flowchart for identifying one or more graphs based on agraph score using the metric-lens combinations of the subset ofmetric-lens combinations. In step 3802, for each metric-lens(es)combination of the subset of metric-lens combinations, the lensparameter selection module 3006 or the processing module 312 constructsa TDA graph. The graph may not be visualized in some embodiments. Invarious embodiments, the lens parameter selection module 3006 selects asubset of resolutions to be used for TDA generation.

The lens parameter selection module 3006 choice of resolution may dependon the number of points in the space (here denoted by N) and the numberof lenses (here denoted by Ln in the metric-lens(es) combination). Sinceresolutions may be multiplicative with the lenses (that is, if there aretwo lenses with resolution 10, there may be 100 buckets), the per-lensresolutions may be adjusted by taking the Ln-th root in someembodiments.

In some embodiments, different lens parameters are given by thisformulas:

-   -   For gain evaluate {2., 3., 4.}    -   For resolution, evaluate for each j in [0, NUM_RES−1] (where        NUM_RES is the number of resolutions to be considered).        res=Math.pow((Math.pow(Math.max(gain*N/(Ln*100.0),10),Ln)+Math.pow((Math.sqrt(N)/4.0)*j,Ln)),1/(double)Ln)

This equation may be written as:

${res} = \left( {\left\lbrack {\max\left( {\frac{{gain}*N}{L_{n}*100},10} \right)} \right\rbrack^{L_{n}} + \left( {\frac{\sqrt{N}}{4}*j} \right)^{L_{n}}} \right)^{\frac{1}{L_{n}}}$

The last resolution value (starting with 0) may be recalled and if resis not at least three greater than the last one resolution, the res maybe skipped. In some embodiments, NUM_RES=20 is enough values toeffectively investigate each metric-lens(es) combination. The number ofparameter combinations for a metric-lens(es) combination may be denotedby PCML. For example, this may be 20*3*2 (uniformized and not). In someembodiments, the formula covers a range of possible gains. It will beappreciated that are many other possible ways of finding and selectingresolutions.

In constructing the TDA graph, as discussed regarding FIG. 3, the inputmodule 314 may receive the original data receives data S. The inputmodule 314 may generate reference space R using metric-lens(es)combination of the subset of metric-lens combinations. The analysismodule 320 may generates a map ref( ) from S into R. The map ref( ) fromS into R may be called the “reference map.” In one example, a referenceof map from S is to a reference metric space R. The map can be describedby one or more lenses (i.e., real valued functions on S). The resolutionmodule 218 generates a cover of R based on the resolution from the lensparameter selection module 3006. The cover of R may be a finitecollection of open sets (in the metric of R) such that every point in Rlies in at least one of these sets. In various examples, R isk-dimensional Euclidean space, where k is the number of filterfunctions. In this example, R is a box in k-dimensional Euclidean spacegiven by the product of the intervals [min_k, max_k], where min_k is theminimum value of the k-th filter function on S, and max_k is the maximumvalue.

The analysis module 320 may cluster each S(d) based on the metric,filter, and the space S. In some embodiments, a dynamic single-linkageclustering algorithm may be used to partition S(d). It will beappreciated that any number of clustering algorithms may be used withembodiments discussed herein. For example, the clustering scheme may bek-means clustering for some k, single linkage clustering, averagelinkage clustering, or any method specified by the user.

The visualization engine 322 may identify nodes which are associatedwith a subset of the partition elements of all of the S(d) forgenerating an interactive visualization. For example, suppose that S={1,2, 3, 4}, and the cover is C1, C2, C3. Then if ref_tags(1)={1, 2, 3} andref_tags(2)={2, 3}, and ref_tags(3)={3}, and finally ref_tags(4)={1, 3},then S(1) in this example is {1, 4}, S(2)={1,2}, and S(3)={1,2,3,4}. If1 and 2 are close enough to be clustered, and 3 and 4 are, but nothingelse, then the clustering for S(1) may be {1} {3}, and for S(2) it maybe {1,2}, and for S(3) it may be {1,2}, {3,4}. So the generated graphhas, in this example, at most four nodes, given by the sets {1}, {4},{1,2}, and {3,4} (note that {1,2} appears in two different clusterings).Of the sets of points that are used, two nodes intersect provided thatthe associated node sets have a non-empty intersection (although thiscould easily be modified to allow users to require that the intersectionis “large enough” either in absolute or relative terms).

Nodes may be eliminated for any number of reasons. For example, a nodemay be eliminated as having too few points and/or not being connected toanything else. In some embodiments, the criteria for the elimination ofnodes (if any) may be under user control or have application-specificrequirements imposed on it. For example, if the points are consumers,for instance, clusters with too few people in area codes served by acompany could be eliminated. If a cluster was found with “enough”customers, however, this might indicate that expansion into area codesof the other consumers in the cluster could be warranted.

The visualization engine 322 may join clusters to identify edges (e.g.,connecting lines between nodes). Once the nodes are constructed, theintersections (e.g., edges) may be computed “all at once,” by computing,for each point, the set of node sets (not ref_tags, this time). That is,for each s in S, node_id_set(s) may be computed, which is an int[ ]. Insome embodiments, if the cover is well behaved, then this operation islinear in the size of the set S, and we then iterate over each pair innode_id_set(s). There may be an edge between two node_id's if they bothbelong to the same node_id_set( ) value, and the number of points in theintersection is precisely the number of different node_id sets in whichthat pair is seen. This means that, except for the clustering step(which is often quadratic in the size of the sets S(d), but whose sizemay be controlled by the choice of cover), all of the other steps in thegraph construction algorithm may be linear in the size of S, and may becomputed quite efficiently.

In step 3806, the group identification module 3008 may identify groupsof nodes of each TDA graph that share a similar outcome (e.g., share ashared characteristic). In some embodiments, the nodes may be colored orotherwise noted as belonging to or associated with one or more outcomes.As discussed herein, the outcomes may be identified in one or morecolumns in data S. Each node may be associated with one or more outcomesin any number of ways.

In some embodiments, if all data points belonging to a node share thesame or similar outcome, the node may be colored or otherwise noted asbelonging or being associated with that same or similar outcome. In someembodiments, the group identification module 3008 may determine that ifthere are a majority of data points of a node (e.g., or an average,median, or mean), then the node will be colored or otherwise noted asbelonging or being associated with that same or similar outcome.

Groups of nodes include nodes that share the same or similar outcomes(or groups of similar distributions of outcomes). Groups of nodes thatshare the same or similar outcomes may be found in any number of ways.In some embodiments, the groups of nodes may be identified usingautogrouping as discussed in FIG. 22.

In some embodiments, autogrouping may be utilized on a weighted graph.In this example, the set that will be autogrouped is the set of nodes ofa weighted graph G. In some embodiments, the autogroup module 2002partitions the graph into groups of nodes that are strongly-connected inthe graph. An unweighted graph may be transformed into a weighted graphif there is a function f on the nodes of the graph. The weight for anedge (a,b) between two nodes a and b in the graph G may be defined to bethe difference between the function values: wt(a,b)=|f(a)−f(b)|. In someembodiments, this graph may be a visualization generated from a data setand the function on the nodes may be given by a color scheme on thenodes. In one example, the autogroup module 2002 may determine theweight for any number of edges in a graph using the distribution of sameor similar outcomes (e.g., shared characteristics) within each node. Theautogrouping process of determining a partition that identifies groupsof nodes that share similar outcomes of data point members is describedwith respect to FIG. 23 herein. The group identification module 3008 mayprovide the identified and/or generated autogrouped partition in areport and/or identify the autogrouped partition in data or a graph.

In step 3808, for each group of each TDA graph, the group score module3010 computes the outcome entropy of the outcome within that group. Invarious embodiments, each graph may comprise any number of groups ofnodes with shared similar or same outcomes. Each group may also beassociated with an outcome entropy computed by the group score module3010.

In step 3810, the graph score module 3012 scores each TDA graph based onthe number of groups int hat particular TDA graph and the computedoutcome entropy of each group in that particular TDA graph. In oneexample, the graph score module 3012 calculates the TDA graph scoreusing the following:

$\left( {{\sum_{{groups}\mspace{14mu} g}{{{entropy}(g)}*\#{{pts}(g)}}} + \frac{N}{50*\#{{pts}(g)}}} \right)*\left\{ {{{{if}\mspace{14mu}\#\;{groups}} < {\#{cat}}},{{then}\frac{\#{cats}}{\#{groups}}},{{else}\mspace{14mu} 1}} \right.$

Groups g represent the number of groups in a particular TDA graph.Entropy (g) is the entropy for the group (g) in that particular TDAgraph which is multiplied by the number of points in the group (g). Thenumber of nodes divided by a constant (e.g., 50) multiplied by thenumber of points in the group is added. It will be appreciated that anyconstant may be used (e.g., between 10 and 90). The score may bemultiplied by the number of outcome categories over the number of groupsif the number of groups is less than the number of outcome categories(to penalize the TDA graph score) or multiply by one, otherwise. It willbe appreciated that each graph may be associated with a TDA graph score.

In various embodiments, the graph selection module 3014 may optionallyrank or order all or some of the graphs using the TDA graph score.

In step 3812, in some embodiments, the graph selection module 3014 mayselect one or more of the graphs, metrics, metric-lens(es) combinations,resolutions, or any combination using the TDA graph score. For example,the graph selection module 3014 may select a predetermined number of TDAgraphs, select TDA graphs with a TDA graph score that meet a certaincriteria (e.g., choose TDA graphs with a TDA graph score over apredetermined threshold), select TDA graphs that are in a toppredetermined percentage, or any other method.

In step 3814, in various embodiments, the outcome visualization module3016 may generate visualizations of one or more selected TDA graphs(e.g., graphs selected by the graph selection module 3014).

For example, the outcome visualization module 3016 may generatevisualizations of one or more selected TDA graphs includingvisualizations of graphs depicted in FIGS. 39A-39D and FIGS. 40A-40D.

FIGS. 39A-39D depict visualizations of selected TDA graphs of Gaussiandata. FIG. 39A may depict a visualization of a graph that is mostpreferable when compared to others, FIG. 39B may depict a visualizationof a graph that is the next most preferable when compared to FIGS. 39Cand 39D. FIG. 39C may depict a visualization of a graph that ispreferable over that of FIG. 39D, however all graphs may containinteresting information having been selected over other metrics,metric-lens(es) combinations, and TDA graphs.

FIG. 39A depicts a visualization of a graph using a Chebyshev(L-Infinity) metric and a neighborhood lens 2 (resolution 61, gain of4.0). As can be shown, the groupings of nodes in the visualization aregrouped by outcome and the groupings show consistent outcomes.

FIG. 39B depicts a visualization of a graph using a variance normalizedEuclidean metric and a neighborhood lens 1 (resolution 57, gain of 3.0).This visualization is similar to FIG. 39A. The groupings of nodes in thevisualization are grouped by outcome and the groupings show consistentoutcomes.

FIG. 39C depicts a visualization of a graph using a variance normalizedEuclidean metric and a neighborhood lenses 1 and 2 (resolution 30, gainof 2.0). The groupings of nodes in the visualization are grouped byoutcome and the groupings shown consistent outcomes.

FIG. 39D depicts a visualization of a graph using a Euclidean (L2)metric and a neighborhood lenses 1 and 2 (resolution 13, gain of 2.0).The groupings of nodes in the visualization are grouped by outcome andthe groupings also show consistent outcomes.

FIGS. 40A-40D depict visualizations of selected TDA graphs of MNISTdata. FIG. 40A may depict a visualization of a graph that is mostpreferable when compared to others, FIG. 40B may depict a visualizationof a graph that is the next most preferable when compared to FIGS. 40Cand 40D. FIG. 40C may depict a visualization of a graph that ispreferable over that of FIG. 40D, however all graphs may containinteresting information having been selected over other metrics,metric-lens(es) combinations, and TDA graphs.

FIG. 40A depicts a visualization of a graph using an Angle metric and aneighborhood lens 1 (resolution 414, gain of 4.0). As can be shown, thegroupings of nodes in the visualization are grouped by outcome and thegroupings show consistent outcomes.

FIG. 40B depicts a visualization of a graph using a Euclidean (L2)metric and neighborhood lenses 1 and 2 (resolution 84, gain of 3.0).This visualization is not similar to FIG. 40A, however, the groupings ofnodes in the visualization are grouped by outcome and the groupings showconsistent outcomes.

FIG. 40C depicts a visualization of a graph using a Manhattan (L1)metric and neighborhood lenses 1 and 2 (resolution 92, gain of 3.0). Thegroupings of nodes in the visualization are grouped by outcome and thegroupings shown consistent outcomes.

FIG. 40D depicts a visualization of a graph using a cosine metric and aneighborhood lens 1 (resolution 282, gain of 4.0). The groupings ofnodes in the visualization are grouped by outcome and the groupings alsoshow consistent outcomes.

In various embodiments, a modeler system may build models quickly,accurately, and perhaps most importantly in the regulated world offinancial services, transparently (e.g., with documentation andinformation). The modeler system may solve complex, high-dimensionalmodeling problems where the goal is to principally reduce the number offeatures/variables being used for modeling purposes thereby facilitatingthe process of building, documenting and deploying those models intoproduction while creating exceptional transparency. The modeler systemmay be particularly effective at risk modeling problems such as, forexample, loss-given default (LGD), probability of default (PD), and/orother regulatory modeling problems. These models may utilize regressiontechniques to support the simplicity and explainability requirementsassociated for regulatory oversight. It will be appreciated that themodeler system may utilize any number of different techniques andapproaches.

FIG. 41 depicts an example workflow 4100 in some embodiments. In step4102, the modeler system may receive data. The modeler system mayprovide options for selection and/or implementation of transformationsthat may derive features from base data and/or may be specific to themodeling problems (e.g., the calculation of log, square, delta lags, andso forth). These transformations may create additional features (e.g.,dimensions or attributes) of the data. A dimension (e.g., a feature) isa structure that organizes data based on categories. In one example,each row of data may represent a data point while each column of datamay represent a dimension.

It will be appreciated that these transformations may be important whencreating models to satisfy and/or meet regulations. For example, a modelform (e.g. linear) may be constrained in many regulatory settings.Without variable enrichment, it may be exceptionally difficult to buildaccurate models. Since the modeler system may distill complexity, theaddition of resources may create better outcomes where other modelingsolutions struggle with high dimensional data.

In many cases, multiple models are required to achieve modelingobjectives. For example, the data might have a grouping variable (suchas country) and the user might want to stratify the data based on thegrouping variable while considering constructing models for each strataindependently. In some embodiments, the modeler system's discoverycapabilities may also be used to discover a stratification of the datain an unsupervised manner.

In step 4104, the modeler system segments features (e.g., dimensions),including, in some embodiments, the base and transformed features, intoself-similar groups. In some embodiments, the modeler system may performTDA to identify features that are similar to each other (e.g., they aresimilarly correlated to an outcome or output). The modeler system maygroup similar features together to enable selection of a reduced numberof features, thereby taking advantage of correlation of a subset offeatures without losing predictability.

In step 4106, a user (e.g., subject matter expert) may choose featuresfrom each group to reduce the overall number of features and therebyreduce the number of models that may be generated without losingpredictability. In various embodiments, the modeler system mayauto-select a combination of features and/or the subject matter expertmay interact with an easy-to-use interface to select the feature(s) thatare meaningful to the business in their estimation.

In various embodiments, every selected feature (and in some casesunselected features) may be associated with a statistical significanceand the list of features may be sorted by correlation. It will beappreciated that not every variable may be noteworthy to the businessleader.

In step 4108, the modeler system may maintain an audit trail of all theselections made by the software and the human operators. It should alsobe noted that the interaction between the data and business users may bea forcing function to consider the various economic hypotheses and buildintuition about the data and the space of models.

In step 4110, the modeler system constructs all possible models from thefeature selections. In various embodiments, the modeler system utilizesoutput data received in step 4102 to assist in testing and fittingdifferent models. Models may be presented in step 4112 in order of fitand/or correlation based on truth data. In various embodiments, themodeler system presents a candidate model and a full suite of challengermodels for business validation. Note that the form of the models createddepends on the business challenge.

In step 4114, the model may be deployed.

FIG. 42 is an exemplary environment 4200 in which embodiments may bepracticed. In various embodiments, topological data analysis, featuregrouping, selection, model training, model training, and/or the like maybe performed locally (e.g., with software and/or hardware on a localdigital device), across a network (e.g., via cloud computing), or acombination of both. Environment 4200 comprises a modeler system 4202, aregulation system 4204, modeling devices 4206 a-n, data sources 4208 a-nthat communication over a communication network 4210. Environment 4200depicts an embodiment wherein functions are performed across a network.In this example, the user(s) may take advantage of cloud computing bystoring data in a data sources 4208 a-n over the communication network4210.

In various embodiments, the environment 4200 may further include ananalysis server (e.g., such as analysis server 208 of FIG. 2) that mayperform data analysis for feature grouping and/or selection.Alternately, in various embodiments, the modeler system 4202 may performany or all analysis (e.g., the modeler system 4202 may performtopological data analysis).

A system (e.g. a modeler system) may include any number of digitaldevices. Further, modeler devices 4206 a-n and data sources 4208 a-n mayinclude one or more digital devices. A digital device is any device thatcomprises memory and a processor. Digital devices are further describedin FIG. 18.

In various embodiments, the modeler system 4202 receives data includingtesting and training data, transforms at least some of the data tochange a data set and/or add new features, performs TDA analysis toidentify and group features by correlations with outputs (e.g., theoutput may be identified by a user as a part of the data), present thegroupings of features to assist in a selection of a reduced number offeatures, generate models based on feature selections and modelparameters, determine correlations of models based on the outputs, makemodel recommendations, track decisions, track correlations for everystep, and display and/or generate a report regarding the recommendedmodel and candidate models (e.g., other models that were generated bynot recommended). The modeler system 4202 may provide documentation ordisplay the decisions, correlations, features, and/or the like for everymodel. For example, the modeler system 3202 may generate an audit reportfor regulatory compliance, model confidence, and/or repeatability.

FIGS. 43a-c depicts a data scientist perspective for model developmentusing the modeler system 4202 in some embodiments. In step 4302 of FIG.43a , a business administrator prepares an objective of a new model. Thebusiness administrator may provide a name of the objective, descriptionof the objective, a model type, due date, and/or a team identifier. Thebusiness administrator may also provide a maximum number of features(e.g., a number of dimensions) to be utilized in the model. The name ofthe objective may be any identifier that identifies the objective and/ormodels to be created. The description of the objective may include, forexample, identification of constraints, regulations, insights, and/orthe like that may impact development of one or more models, auditinformation, regulation information, and/or regime(s) for testing one ormore models. The model type may indicate any type of model (e.g., modeltypes are discussed further herein).

Due date may indicate when the model should or must be created. It willbe appreciated that in the prior art, model creation and development maytake months or longer to develop, train, and test. In variousembodiments, the modeler system 4202 may group features for selectionand enable development of a variety of models to satisfy the objectivein minutes or hours.

The responsibility for model creation may be assigned to a lead modeler,one or more collaborators, and/or one or more reviewers. In variousembodiments, the modeler system 4202 may perform the assignments. Forexample, the business administrator may provide the name of theobjective, description of the objective, model type, due date, and/orteam identifiers to the modeler system 4202. The modeler system 4202 mayreceive identifiers of the lead modeler, the one or more collaborators,and/or the one or more reviewers. In some embodiments, the modelersystem 4202 may make assignments based on the team identifier (e.g., thelead modeler, collaborator(s), and/or reviewer(s) maybe members of ateam associated with the team identifier).

In step 4304, the lead modeler may receive or provide raw data (e.g.,historical revenue, borrower information, macro market data, and/or thelike), scenario data, output data, script selections, and/or modelparameters. In various embodiments, the lead modeler may receive theobjectives provided by the business administrator and, using aninterface of the modeler system 4202, the lead modeler may provide dataidentifiers to identify the raw data (e.g., location of the raw data orretrieve), scenario data (e.g., data associated with specific scenariosto test), and output data (e.g., the portion of the data associated withoutcome). Based on the objective the lead modeler may provide scriptselections for ETL, data transformations, and/or analysis. The leadmodeler may also provide the modeler system 4202 with model parameterssuch as regression type, cost function type, and/or regulation setting).Based on the information provided by the lead modeler, the modelersystem 4202 may perform analysis (e.g., TDA analysis, outcome analysis,and/or autogrouping to identify and group similar features).

The modeler system 4202 may group similar features and rank the groupsbased on correlation to the identified output. The groups may bepresented to a modeling team. In step 4306 of FIG. 43b , one or moreusers of the modeling team may select features from any number of groupsto build models. The modeler system 4202 may build models based on theselected features. In various embodiments, the modeler system 4202 maybuild models based on different combinations of the selected featuresand then determine model fit to the previously identified output, groundtruth, and/or scenario data. A candidate model may be recommended aswell as other draft models.

One or more members of the modeling team may review the draft models.When the member(s) review the draft model, the member(s) may examine anynumber of the models, explore scenarios (e.g., how the model handlesscenario data), and add comments. The member(s) may select a model.

In various embodiments, the member(s) may create additional featuresusing transformation, share data sources and models with other teammembers, review other team member drafts models, and/or the like.

In step 4308 of FIG. 43c , a review committee may review the objectiveincluding the models represented by the member(s) and provide therecommendation including documentation regarding selections,correlations, grouping, and/or the like to the business administratorfor review and/or deployment.

Returning to FIG. 42, in various embodiments, the modeling devices 4204a-n may be any number of digital devices utilized by the businessadministrator, modeling team (e.g., lead modeler), team members,collaborator(s), and/or reviewer(s). In various embodiments, one or moremodeling devices 4206 a-n may interact with the modeler system 4202and/or analysis system (see FIG. 2).

The regulation system 4204 may include any number of digital devicesconfigured to provide regulations, audit models based on need and/orregulations, and the like. In various embodiments, the regulation system4204 is operated by a government agency or any entity that may wish toreview models generated by the modeler system 4202, documentation,reasons and decision points documented by the modeler system 4202,scenario results, and/or the like. In various embodiments, theregulation system 4204 may receive reports from the modeler system 4202and/or interact with one or more dashboards generated by the modelersystem 4202.

In some embodiments, the regulation system 4204 may provide modelparameters, recommendations, and/or requirements to any number of themodeler devices 4206 a-n and/or the modeler system 4202.

The modeler system 4202 may receive or retrieve data from one or moredata sources 4208 a-n. Data sources 4208 a-n may include any number ofdigital devices that store any amount of data. In various embodiments,the modeler system 4202 may generate one or more data sets from datareceived from or retrieved from data sources 4208 a-n. The data sources4208 a-n may maintain data in local storage, network storage, cloudstorage, and/or the like. Although FIG. 42 depicts data sources 4208a-n, it will be appreciated that the modeler system 4202 may receive anyamount of data locally (e.g., in contrast with receiving data over anetwork), or both locally and over a network.

The data sources 4208 a-n may be or include a single server or acombination of servers. In one example, the data sources 4208 a-n may bea secure server wherein a user may store data over a secured connection(e.g., via https). The data may be encrypted and backed-up. In someembodiments, a data source 4208 is operated by a third-party such asAmazon's S3 service.

One or more data sources 4208 a-n may contain data in a database orother data structure. The database or other data structure may compriselarge high-dimensional datasets. These datasets are traditionally verydifficult to analyze and, as a result, relationships within the data maynot be identifiable using previous methods. Further, previous methodsmay be computationally inefficient.

The communication network 4210 may be any network that allows digitaldevices to communicate. The communication network 4210 may be theInternet and/or include LAN and WANs. The communication network 4210 maysupport wireless and/or wired communication.

In various embodiments, the environment 4200 may include an analysissystem. The analysis system may include the analysis server 208described with regard to FIG. 2. In various embodiments, the modelersystem 4202 may perform one or more functions of the analysis server 208(e.g., the modeler system 4202 may perform TDA analysis).

The analysis system may be any number of digital devices configured toanalyze data. In various embodiments, the analysis system may performmany functions to interpret, examine, analyze, and display data and/orrelationships within data. In some embodiments, the analysis systemperforms, at least in part, topological analysis of large datasetsapplying metrics, filters, and resolution parameters chosen by the user.The analysis is further discussed regarding in FIG. 8 herein.

FIG. 44 depicts an example modeler system 4202 in some embodiments. Themodeler system 4202 may include a model definition module 4402, a dataimport module 4404, a feature derivation module 4406, a featureselection module 4408, an optional feature reduction module 4410, amodel generation module 4412, a model fitting module 4414, a modelrecommendation module 4416, a scenario module 4418, an interface module4420, and a documentation module 4422. The feature derivation module4406 may include an analysis engine 4424, an outcome analysis engine4426, and an autogrouping engine 4428.

In various embodiments, functions of the modeler system 4202 may begrouped in two phases. In the first phase, the modeler system 4202receives data (e.g., including output columns), identifies features fromthe data, performs TDA on a transpose of the data to group featuresbased on similarity, and provide the groupings to reduce the number ofpossible features for model creation. By grouping features based onsimilar effect and scoring against the output of the data, moreeffective features may be recommended. Features that are grouped mayperform similar functions and, as a result, a representation of thegroup may be selected (e.g., one or two features representing the effectof the group) without losing predictability rather than selecting allfeatures of any number of groups which can be computationallyinefficient or impossible to generate within a reasonable time.

In the second phase, the modeler system 4202 may build any number ofmodels based on the selected features and the models may be tested. Insome embodiments, the modeler system 4202 determines fit of each modelto an output (e.g., outcome), understood true outcomes (e.g., groundtruth), and/or scenarios. The modeler system 4202 may then recommend amodel and other candidate models based on the determination.

It will be appreciated that the modeler system 4202 may carefullydesigned application workflow and collaboration features allow the modeldesigner to anchor discussion while facilitating reviews with line ofbusiness owners and internal review managers on statistically defensibledecisions. Once a base model has been created, a full suite ofsupporting or alternative models are being generated in parallel,enabling “real time” and interactive iteration while all stakeholdersare in the same meeting.

In one example, the 2008-2009 financial collapse led to a FederalReserve directive that banks with consolidated assets over $50 billionhave additional risk assessment frameworks and budgetary oversight inplace. To assess a bank's financial foundation, the Federal Reserveoversees a number of scenarios (company-run stress tests). Referred toas the Comprehensive Capital Analysis and Review (CCAR) process, thesetests are meant to measure the sources and use of capital under baselineas well as stressed economic and financial conditions to ensure capitaladequacy in all market environments.

A Fortune 50 bank was consistently struggling to pass its annual stresstest. The bank needed a way to rapidly create accurate, defensiblemodels that would prove to the Federal Reserve that they couldadequately forecast revenues and the capital reserve required to absorblosses under stressed economic conditions. The bank's modeling approachleft the business unit leads with little room and time to weigh in onthe logic behind the choice of the variables selected. The result wasthat could not confidently defend the models that they included in thefilings they presented to the Federal Reserve.

In this example, the process began with the leaders of the bank'sbusiness units reviewing the macroeconomic variables stipulated by theFederal Reserve. Transformations were identified to generate newfeatures. For example, the transformations enriched the macroeconomicvariables using several techniques (e.g., time series transforms such aslags, differences, and percent changes) and created over two thousandvariables.

The modeler system may enable grouping of features (e.g., utilizing TDA,outcome analysis, and/or autogrouping) of similar features, ordering ofthe groupings based on feature correlation with an output of the data,and allowing users to select a subset of the features from the groupsthereby allowing reduction of features while maintaining predictability.Models may be generated based on the selected features and the modelstested for fit to the output of the data (e.g., the same output thatfeature correlation utilized above).

The model definition module 4402 may receive locations of analysis data,locations of scenario data, transformation identifiers that identifytransformations to apply to the analysis data, an output identifier thatidentifies output contained within the analysis data, and the like. Theoutput identifier may be utilized to determine similarity of features(e.g., dimensions), correlations with features, and model fitting.

In various embodiments, the model definition module 4408 may receivemodel parameters. The model parameters may include model type (e.g.,linear regression, logistic regression, svm), cost function (e.g.,quantile, hinge, least absolute deviation, square loss function), andregularization (e.g., none, L-1, L-2).

In various embodiments, the model definition module 4402 may receivemodel parameters including, for example, a type model to create (e.g.,linear or quadradic regression), cost function, and/or regularization.It will be appreciated that any model or combination of models may begenerated. Similarly, different cost functions and/or regularization maybe utilized in the same and/or different models.

In some embodiments, the model definition module 4402 may receivetransformation identifiers that indicate different transformations thatmay be performed on the analysis data. For example, the model definitionmodule 4402 may receive a transpose identifier indicating that thetranspose of the analysis data needs to be generated. In variousembodiments, any number of functions may be performed on features and/ordata associated with features to create new features.

The model definition module 4402 may receive scenario files indicatingscenario data. The scenario data may include regulatory mandated orinternally generated scenarios for testing. In various embodiments, themodel definition module 4402 may also receive ground truth or objectivefiles from existing data for testing. In some embodiments, the groundtruth may represent data that is set aside from the analysis data.

The data input module 4404 may be configured to retrieve analysis dataand/or scenario data from data sources (e.g., data sources 4208 a-n) foranalysis based on information from the model definition module 4402. Invarious embodiments, the data input module 4404 may performtransformations discussed herein on any or all of the features togenerate new features that are added to the analysis data. Exampletransformations may include, for example, point-in-time transformationsand binning time-series events. The data input module 4404 may also takethe transpose of the data re-orient columns as rows (e.g., to takefeatures, including the additional features from the transformations, ordimensions of the data and treat them as data points for TDA analysis).

The feature derivation module 4406 may utilize TDA, outcome analysis,and/or autogrouping to identify features that are similar and group themtogether. In some embodiments, the feature derivation module 4406 usestopological data analysis to generate a topological model (perhaps withoutcome automatic analysis discussed herein) on the transpose of theoriginal data together with auto-grouping (discussed herein) to generategroups of features that are similar. Each group of features may beattached with a numerical quality score (e.g., a correlation to theoutput, outcome, truth, or scenario data). The feature derivation module4406 may continue with this analysis if the groups are of high qualityor to choose to refine the groups further.

In various embodiments, features from one of these groups are likely tohave similar characteristics within a model; as such, choosing a singlefeature from each group for model testing significantly reduces thenumber and complexity of possible models that need to be generated.Groups may be ranked by the confidence score and each variable is sortedby the output measure (e.g., correlation).

The feature derivation module 4406 may utilize TDA to rapidly correlateand analyze the impact of these variables on each business unit'smonthly revenue performance over a six-year period, uncoveringstatistically significant variables that were highly correlated withoutput identified by the or received from the model definition module4402. The output identifier received by the model definition module 4402may indicate a column or row of data that is associated with an outputor outcome. The feature derivation module 4406 may utilize TDA, outcomeanalysis, and/or autogrouping using all or some of the data associatedwith the output identifier. In various embodiments, the featurederivation module 4406 utilizes TDA, outcome analysis, and/orautogrouping to identify similarity of features from the analysis data,group similar features (e.g., in comparison with the identified outputof the analysis data), and determine correlation with the outcome foreach feature.

In various embodiments, the data input module 4404 may separate theanalysis data into in-sample data for training and out-sample data fortesting. In one example, the data input module 4404 may divide the datainto 80% of the originally received data for in-sample data and 20% ofthe originally received data for out-sample data (although It will beappreciated that that any percentage or division may be utilized). Insome embodiments, there may be separate data for testing and theoriginally receive data is not divided. Further, in other embodiments,there may be no separate data. For TDA, model generation, and modelfitting, the modeler system 4202 may utilize the in-sample data of theanalysis data (e.g., for scoring of outcome analysis and/orautogrouping). For testing models, the modeler system 4202 may utilizethe out-sample data for ground truth.

The feature derivation module 4406 may utilize TDA to assist in groupingsimilar features of the transpose analysis data. In various embodiments,the feature derivation module 4406 uses topological data analysis tolook at all possible relationships encoded within the enrichedbase-level data; finding hidden patterns that hold predictive value.These patterns are frequently obscured and cannot be detected by otherapproaches. In some embodiments, by taking an unsupervised approach andlooking at all the data enterprises can often validate or disproveorganizational assumptions while becoming more sensitive toenvironmental changes impacting the business. This approach also avoidsmodeling pitfalls such as multicollinearity and the tendency of forwardor backward selection methods resulting in local minima.

While the feature derivation module 4406 has capabilities inunsupervised learning, often model developers and line of businessleaders want explicit control over the model through the selection ofkey variables. This approach may be fully supported in modeler system4202 but goes one step further by documenting (e.g., by thedocumentation module 4422) bias associated with the selection of certainvariables and the elimination of others. This leads to better models andgreater transparency.

The feature selection module 4408 may present an ordered set of featuregroups. Each feature group may include members of features that aresimilar to each other (e.g., by correlation to output or other databased, at least in part, on TDA for example). The feature selectionmodule 4408 may determine a group correlation (e.g., an average ofcorrelations of each member of the group) and order the groups based onthe group correlation. The feature selection module 4408 may furtherpresent an interface including the ordered groups, individual featuresof each group, the group correlations, and/or the feature correlation toenable a user to select features for model generation.

It will be appreciated that the user may select a subset of features ofa group. The selected subset of features may capture the predictabilityof other non-selected but similar features while reducing the number offeatures to be included in the model. This model simplification capturesthe impact of the group while potentially meeting requirements of themodel (e.g., a maximum of six features for the model), meetingregulatory requirements, meeting business objectives, and/or improvingcomputational efficiency without significant reduction ofpredictability.

Frequently, the modeling requirements demand both a global model andlocal models. This frequently occurs geographically (e.g. Asia and thecomponent countries) but can be found in any number of situations. Thelocal models have the same sets of variables as the global models,however the interaction among those variables can be fundamentallydifferent. The feature selection module 4408 may enable construction ofmodels at both granularities rapidly and with fulldocumentation—enabling a more detailed understanding of both the entireregion and the local markets.

In various embodiments, by identifying features to be selected includingcorrelation, groupings, and other information regarding features formodel creation, the feature selection module 4408 enables businessreview to screen the identified features prior to inclusion in themodels for each business unit.

In some embodiments, the feature reduction module 4410 may receiveselections from the feature selection module 4408 and may further assessthe most significant correlations within each group to further reduce anumber of features of one or more groups. The feature reduction module4410 may re-run TDA, outcome analysis, and/or autogrouping on theselected features to assess correlation with output data and select asubset of selected features any number of the feature groups. Forexample, the feature reduction module 4410 may receive a selection ofthree features which are all members of a group that contains four totalfeatures. Since the selected features are similar to each other, thefeature reduction module 4410 may further assess the three selectedfeatures to determine the most significant feature of the three toselect (e.g., based on correlation and/or similarity with other membersof the group). In some embodiments, the feature reduction module 4410may not remove a feature such that a group that previously containedselected features may no longer be represented for generation of themodels.

In the second phase, the modeler system 4202 generates models usingdifferent selected features and determines fit of the generated modelsto output and/or scenario data. The modeler system 4202 may document theselections, fit, correlations, features used, and/or the like for anynumber of the models.

The model generation module 4412 may generate a variety of models andtest the models against the output of the analysis data (e.g., theout-sample data of the analysis data). In one example, if there are sixfeatures selected and only a maximum of four features allowed for amodel, the model fitting module 4414 may generate every possiblecombination of models based on the selected features with the constraintof a maximum number of features for a model.

The model generation module 4412 may utilize the selected featurestogether with the model parameters specified earlier to build manymodels for evaluation and then train the resulting models using asubsample of the original, non-transposed data.

The model fitting module 4414 may determine a quality of fit of eachmodel to determine confidence and correlation. In various embodiments,the model fitting module 4414 tests models using ground truth (e.g.,from a subset of the analysis data) to test the model(s) and forvalidation. The model fitting module 4414 may determine confidence andfit in comparison to ground truth, scenario data, and/or output data.

The model fitting module 4414 may rank the resulting models by the lossfunction values and shows a set of in-sample and out-sample statisticsalong with a visualization of out-of-sample performance such asprediction vs. actual scatter-plot or ROC curve. In various embodiments,the model fitting module 4414 determines scenario performance,coefficients and variables, and/or accuracy statistics.

The model recommendation module 4416 may identify a recommend model incomparison to other models based on the confidence, fit, correlation,and/or the like. The model recommendation module 4416 may also recommendother candidate models for further analysis and/or comparison. At thispoint, the user can choose to select a subset of these models anddesignate those as candidate models. The candidate models are thenvisible to designated reviewers.

The scenario module 4418 may be configured to receive scenario data andto apply the recommended and/or candidate models to the scenario data togenerate predictions and/or confirm against truth data. In variousembodiments, the scenario module 4418 may perform scenario forecastingbased on internal or regulatory scenarios provided in the scenario data(e.g., scenario projections).

The scenario data may include measures and other information thatindicate possible situations or scenarios. For example, while theanalysis data may include historical data, the scenario data may includedata of a particular scenario (e.g., values of features indicating afinancial regime such as a stock market crash of a given severity).

The interface module 4420 may generate interfaces to receive data,transformations, output identification, model parameters, and/or thelike. The interface module 4420 may also generate interfaces to depictfeature groupings, orderings of feature groupings, receive featureselections, recommend models, and depict related information asdiscussed herein. In some embodiments, the interface module 4420 maydisplay all or part of the detailed model comparison report.

In various embodiments, the interface module 4420 may generate anynumber of interfaces. For example, the interface module 4420 maygenerate an interface to receive information regarding data, informationregarding a model, and/or the like. In some embodiments, the interfacemodule 4420 may generate an interface for a business administrator. Thebusiness administrator may utilize a model generation initiationinterface generated by the interface module 4420 to provide the name ofthe objective, description of the objective, model type, due date,and/or team identifiers. The business administrator may provideidentifiers of the lead modeler, the one or more collaborators, and/orthe one or more reviewers to the modeler system 4202 through the modelgeneration initiation interface. In some embodiments, the modeler system4202 may make assignments to different digital devices and/or usersbased on the team identifier (e.g., the lead modeler, collaborator(s),and/or reviewer(s) maybe members of a team associated with the teamidentifier).

In various embodiments, the model definition module 4402 may receiveinformation from the model generation initiation interface including,for example, the name of the objective, description of the objective,model type, due date, and/or team identifiers. In various embodiments,the model definition module 4402 may provide any or all of theinformation to one or more other users (e.g., by email or otherelectronic communication) including, for example, a lead modeler,collaborator(s), and/or reviewer(s).

The interface module 4420 may generate a data and model parameterinterface. The data and model interface may enable a user to provideinformation such as addresses or locations to retrieve analysis data(e.g., raw data or data that has been processed) and scenario data. Theanalysis data may be utilized to identify features in the analysis data,group the features, and determine correlation of features to an output(e.g., an outcome). Scenario data may include any number of features ofthe analysis data. In various embodiments, the analysis data includeshistorical data. The data and model parameter interface may also includeoutput data (e.g., outcome data). In various embodiments, a user mayidentify the output data (e.g., a column or feature) contained withinthe analysis data. As discussed herein, the modeler system 4202 maydetermine correlation between any number of features of the analysisdata and the identified output data.

The documentation module 4422 may document decisions and data to loginformation and create reports regarding models, the model makeup (e.g.,selected features), decisions regarding the model, unselected features,and the like.

The documentation module 4422 may generate a detailed model comparisonreport as discussed herein. The model comparison report may include theresults of coefficient sensitivity analysis performed by the modelfitting module 4414 and/or the scenario module 4418 and simulation ofthe output. Stress-testing provides the coefficient distribution andcoefficient importance for each coefficient in the model.

During the model creation process, the documentation module 4422 may loginformation automatically. The documentation module 4422 mayinstitutionalize both feature selection and modeling methodology,systematically and deterministically, to produce a repeatable processwith consistent supporting reports on model lineage, feature selectionand cross validation

Not only are the initial selections recorded and documented, the entiremodeling and approval process may be tracked and cataloged for all modeldevelopers and collaborators-facilitating everything from review tomodel re-use.

Generally, many models historically are created by starting from theprevious year's model and adjusted based on the data they containimplicit bias associated with both variable and algorithm selection.While known initially, over time, this bias is often obscured throughrevision. The documentation module 4422 documents the decisions andmodels over time to assist in solving this challenge and can ensure thatan enterprise has better insight into the behavior of models and theirinteraction with other models or processes within the business.

This is particularly important in a regulatory setting where performancemay dictate changing models over time. Further, the ability todemonstrate the rigor of evaluating challenger models is also of valueto the enterprise when explaining the models.

FIG. 43d depicts an example process showing how reduction of features tothose that will be most relevant to output (and prediction) may beutilized to reduce a number of relevant models that are generated andevaluated. Graphic 4310 depicts that the original data includes 600features. As a result, there are 2600 different possible models.Generation of each of those models for evaluation is computationallyinefficient and potentially impossible (e.g., due to the amount of timerequired).

Graphic 4312 depicts that a grouping similar features (e.g., as a resultof applying TDA to determine similarity of effect of features) can beused to reduce the potential number of models to 2115. The groups may beranked in terms of correlation with output identified in the data.

Graph 4314 depicts that a selections of a subset of groups (e.g., onefrom each group) can further reduce the number of potential models to120. After the features are selected, the modeler system 4204 maygenerate different models based on different combinations of selectedfeatures. The models may be evaluated and/or ranked based oncorrelations with the output data.

Graph 4316 depicts, in this example, that models are created and ofthose models, based on statistical tests and/or evaluation (e.g.,correlation with output or testing data), that there are twenty possiblemodels.

Graph 4318 depicts, in this example, that five candidate models arerecommended based on the ranking and/or correlations.

Graph 4320 depicts that one final model is selected with two“challengers” or other candidate models. The selected final model andcandidate models may be based on ranking, correlations, documentation,and/or expertise of a user in the field of the objective being modeled.

FIGS. 45a-b is a flowchart for feature grouping, reduction, modelgeneration, and model recommendation in some embodiments. In step 4502,the model definition module 4402 receives an analysis data identifier(e.g., a location to retrieve analysis data or the analysis data itself)and an output indicator (e.g., a portion of the analysis data such as acolumn). FIG. 46 depicts an example data and model parameter interface4600 for a probability of default (PD) model. In this example, the dataand model parameter interface 4600 indicates that a model is due Jul. 8,2016, and the model must have no more than four features (e.g., no morethan four dimensions). The owner (e.g., business administrator) isidentified as Pete Miraz and the project was assigned to Julia Cohen.Collaborators include Mark Nesbit and Karen Newell. Reviewers includeScott Kao, Perry Vonk, and Elizabeth Garriss. In this example, the modelparameters include logistic regression, a quantile cost function, and L1regularization.

The data and model parameter interface 4600 may enable a user (e.g., theassigned user, collaborator(s), and/or reviewer(s) to configure modelparameters such as model type 4602 (e.g., logistic regression), costfunction 4604 (e.g., square loss cost function and/or quantile costfunction), and regularization 4606 (e.g., L1 regularization).

The data and model parameter interface 4600 may enable a user to provideinformation regarding the analysis data 4608, scenario data 4610, outputdata 4612, and/or transformations. For example, the model definitionmodule 4402 may receive any number of locations to retrieve the analysisdata 4608. Similarly, the model definition module 4402 may receivescenario data 4610 from any number of locations. The output data 4612may indicate any number of column(s) or feature(s) of the analysis dataassociated with an output or outcome.

The data and model parameter interface 4600 may also include a field toreceive transformation identifiers that indicate at type oftransformation to apply to any or all of the analysis data. For example,the transformation identifier may indicate that the data input module4404 is to take a transpose of the analysis data. In another example,the transformation identifier may indicate one or more functions togenerate any number of new features based on one or more previouslyexisting features of the analysis data.

In step 4504, the data input module 4404 may retrieve the analysis datafrom any number of data sources 4208 a-n based on the analysis dataidentifier. The analysis data may include any number of data sets fromany number of sources. In various embodiments, the analysis data may bea transpose and/or include processing/structuring of other data.

In some embodiments, the data input module 4404 may retrieve scenariodata from any number of data sources 4206 a-n. Scenario data mayinclude, for example, data indicative a possible future, scenario,circumstance, or the like (e.g., a financial collapse). A given modelmay utilize scenario data for testing, prediction, regulatorycompliances, and/or the like

In step 4506, the data input module 4404 may perform any number oftransformations on the analysis data and take the transpose of theanalysis data. In various embodiments, the data input module 4404 mayperform data transformations on any or all of the analysis data. In oneexample, after the data input module 4404 generates new features usingany number of transformations and adds the new features to the analysisdata, the data input module 4404 may take the transpose of the analysisdata to rotate the data set such that the previous dimensions of thedata set are now data points.

FIG. 47 depicts an example of transposed, transformed, analysis data. Inthis example, the first column indicates the set of features of theanalysis data which, due to the transposition of the original data, nowindicates data points. Data in the transposed, transformed, analysisdata indicates values as of specific days.

It will be appreciated that that initial features of the analysis datamay have been transformed to add additional features. For example,transformations identified via the data and model parameter interface4600 may have been performed on the analysis data to generate a delta,lag1, lag1 delta, lag1square, lag2, lag2delta, lag2square, and a square.In some embodiments, additional features are generated for every initialfeature of the analysis data. For example, similar transformations mayoccur to the 10 y Treasury Yield-basic, 3 M Treasury Yield-basic, 5 YTreasury Yield-basic, BBB Corporate Yield-basic, and the like. It willbe appreciated that the transposed, transformed analysis data mayinclude any number of data points (initial features of the analysis databefore transposed).

FIG. 48 depicts an example of scenario data. Scenario data includesscenarios for certain dates as data points. Features (e.g., dimensions)include 10 Y Treasury Yield_basic, 10 Y Treasury Yield_delta, 10 YTreasury Yield_lag1, 10 Y Treasury Yield_lag1Delta, 10 Y TreasuryYield_lag1Square, 10 Y Treasury Yield_lag2, 10 Y TreasuryYield_lag2Delta, and 10 Y Treasury Yield_lag2Square. It will beappreciated that the scenario data may include any number of scenariosand any number of features. In some embodiments, the scenario data mayinclude scenario output data for testing or may not include any scenariooutput data.

In step 4508, the feature derivation module 4406 may perform topologicaldata analysis on the transposed, transformed, analysis data to determinesimilarity of features (e.g., data points in a transpose of the originalanalysis data). It will be appreciated that the transposed, transformed,analysis data may be termed simply as “analysis data” herein. Indetermining similarity, the feature derivation module 4406 may determinedistances for each row against one or more other rows. As a result, TDAmay determine similarity in multi-dimensional data

FIG. 49 is a flowchart 4900 for data analysis in some embodiments. FIG.49 may be similar to FIG. 8. In various embodiments, the processing ondata and user-specified options is motivated by techniques from topologyand, in some embodiments, algebraic topology. These techniques may berobust and general. In one example, these techniques apply to almost anykind of data for which some qualitative idea of “closeness” or“similarity” exists. The techniques discussed herein may be robustbecause the results may be relatively insensitive to noise in the data,user options, and even to errors in the specific details of thequalitative measure of similarity, which, in some embodiments, may begenerally refer to as “the distance function” or “metric.” It will beappreciated that while the description of the algorithms below may seemgeneral, the implementation of techniques described herein may apply toany level of generality.

In step 4902, the feature derivation module 4406 receives transposedanalysis data which is denoted as data S. In various embodiments, data Sis treated as being processed as a finite “similarity space,” where dataS has a real-valued function d defined on pairs of points s and t in S,such that:d(s,s)=0d(s,t)=d(t,s)d(s,t)>=0

-   -   These conditions may be similar to requirements for a finite        metric space, but the conditions may be weaker. In various        examples, the function is a metric.

It will be appreciated that data S may be a finite metric space, or ageneralization thereof, such as a graph or weighted graph. In someembodiments, data S be specified by a formula, an algorithm, or by adistance matrix which specifies explicitly every pairwise distance.

In step 4904, the feature derivation module 4406 generates referencespace R. In one example, reference space R may be a well-known metricspace (e.g., such as the real line). The reference space R may bedefined by the modeler or a number of resolutions may be tested. In step4906, the feature derivation module 4406 generates a map ref( ) from Sinto R. The map ref( ) from S into R may be called the “reference map.”

In one example, a reference of map from S is projected to a referencemetric space R. R may be Euclidean space of some dimension, but it mayalso be the circle, torus, a tree, or other metric space. The map can bedescribed by one or more lens functions (i.e., real valued functions onS). A lens functions can be any function that is applied to each valueacross multiple dimensions for each row (e.g., for each data point).These lens functions can be defined by geometric invariants, such as theoutput of a density estimator, a notion of data depth, or functionsspecified by the origin of S as arising from a data set. In someembodiments, the feature derivation module 4406 may perform multipleanalysis using different lens functions.

In step 4908, the feature derivation module 4406 generates a cover of Rbased on a resolution value or resolution function. The cover of R maybe a finite collection of open sets (in the metric of R) such that everypoint in R lies in at least one of these sets. In various examples, R isk-dimensional Euclidean space, where k is the number of filterfunctions. More precisely in this example, R is a box in k-dimensionalEuclidean space given by the product of the intervals [min_k, max_k],where min_k is the minimum value of the k-th filter function on S, andmax_k is the maximum value.

For example, suppose there are 2 filter functions, F1 and F2, and thatF1's values range from −1 to +1, and F2's values range from 0 to 5. Thenthe reference space is the rectangle in the x/y plane with corners(−1,0), (1,0), (−1, 5), (1, 5), as every point s of S will give rise toa pair (F1(s), F2(s)) that lies within that rectangle.

In various embodiments, the cover of R is given by taking products ofintervals of the covers of [min_k,max_k] for each of the k filters. Inone example, if the user requests 2 intervals and a 50% overlap for F1,the cover of the interval [−1,+1] will be the two intervals (−1.5, 0.5),(−0.5, 1.5). If the user requests 5 intervals and a 30% overlap for F2,then that cover of [0, 5] will be (−0.3, 1.3), (0.7, 2.3), (1.7, 3.3),(2.7, 4.3), (3.7, 5.3). These intervals may give rise to a cover of the2-dimensional box by taking all possible pairs of intervals where thefirst of the pair is chosen from the cover for F1 and the second fromthe cover for F2. This may give rise to 2*5, or 10, open boxes thatcovered the 2-dimensional reference space. However, it will beappreciated that the intervals may not be uniform, or that the covers ofa k-dimensional box may not be constructed by products of intervals. Insome embodiments, there are many other choices of intervals. Further, invarious embodiments, a wide range of covers and/or more generalreference spaces may be used.

In one example, given a cover, C1, . . . , Cm, of R, the reference mapis used to assign a set of indices to each point in S, which are theindices of the Cj such that ref(s) belongs to Cj. This function may becalled ref_tags(s). In a language such as Java, ref_tags would be amethod that returned an int[ ]. Since the C's cover R in this example,ref(s) must lie in at least one of them, but the elements of the coverusually overlap one another, which means that points that “land near theedges” may well reside in multiple cover sets. In considering the twofilter example, if F1(s) is −0.99, and F2(s) is 0.001, then ref(s) is(−0.99, 0.001), and this lies in the cover element (−1.5,0.5)×(−0.3,1.3). Supposing that was labeled C1, the reference map mayassign s to the set {1}. On the other hand, if t is mapped by F1, F2 to(0.1, 2.1), then ref(t) will be in (−1.5,0.5)×(0.7, 2.3), (−0.5,1.5)×(0.7,2.3), (−1.5,0.5)×(1.7,3.3), and (−0.5, 1.5)×(1.7,3.3), so theset of indices would have four elements for t.

Having computed, for each point, which “cover tags” it is assigned to,for each cover element, Cd, the points may be constructed, whose tagsincluded, as set S(d). This may mean that every point s is in S(d) forsome d, but some points may belong to more than one such set. In someembodiments, there is, however, no requirement that each S(d) isnon-empty, and it is frequently the case that some of these sets areempty. In the non-parallelized version of some embodiments, each point xis processed in turn, and x is inserted into a hash-bucket for each j inref_tags(t) (that is, this may be how S(d) sets are computed).

It will be appreciated that the cover of the reference space R may becontrolled by the number of intervals and the overlap identified in theresolution (e.g., see FIG. 7). For example, the more intervals, thefiner the resolution in S—that is, the fewer points in each S(d), butthe more similar (with respect to the filters) these points may be. Thegreater the overlap, the more times that clusters in S(d) may intersectclusters in S(e)—this means that more “relationships” between points mayappear, but, in some embodiments, the greater the overlap, the morelikely that accidental relationships may appear.

In step 4910, the feature derivation module 4406 clusters each S(d)based on the metric, filter, and the space S. In some embodiments, adynamic single-linkage clustering algorithm may be used to partitionS(d). It will be appreciated that any number of clustering algorithmsmay be used with embodiments discussed herein. For example, theclustering scheme may be k-means clustering for some k, single linkageclustering, average linkage clustering, or any method specified by theuser.

In some embodiments, a filter may amount to a “forced stretching” in acertain direction. In some embodiments, the analysis module 320 may notcluster two points unless all of the filter values are sufficiently“related” (recall that while normally related may mean “close,” thecover may impose a much more general relationship on the filter values,such as relating two points s and t if ref(s) and ref(t) aresufficiently close to the same circle in the plane). In variousembodiments, the ability of a user to impose one or more “criticalmeasures” makes this technique more powerful than regular clustering,and the fact that these filters can be anything, is what makes it sogeneral.

The output may be a simplicial complex, from which one can extract its1-skeleton. The nodes of the complex may be partial clusters, (i.e.,clusters constructed from subsets of S specified as the preimages ofsets in the given covering of the reference space R).

In step 4912, the feature derivation module 4406 identifies nodes whichare associated with a subset of the partition elements of all of theS(d) for generating an interactive visualization. For example, supposethat S={1, 2, 3, 4}, and the cover is C1, C2, C3. Then ifref_tags(1)={1, 2, 3} and ref_tags(2)={2, 3}, and ref_tags(3)={3}, andfinally ref_tags(4)={1, 3}, then S(1) in this example is {1, 4},S(2)={1,2}, and S(3)={1,2,3,4}. If 1 and 2 are close enough to beclustered, and 3 and 4 are, but nothing else, then the clustering forS(1) may be {1}{3}, and for S(2) it may be {1,2}, and for S(3) it may be{1,2}, {3,4}. So the generated graph has, in this example, at most fournodes, given by the sets {1}, {4}, {1,2}, and {3,4} (note that {1,2}appears in two different clusterings). Of the sets of points that areused, two nodes intersect provided that the associated node sets have anon-empty intersection (although this could easily be modified to allowusers to require that the intersection is “large enough” either inabsolute or relative terms).

Nodes may be eliminated for any number of reasons. For example, a nodemay be eliminated as having too few points and/or not being connected toanything else. In some embodiments, the criteria for the elimination ofnodes (if any) may be under user control or have application-specificrequirements imposed on it. For example, if the points are consumers,for instance, clusters with too few people in area codes served by acompany could be eliminated. If a cluster was found with “enough”customers, however, this might indicate that expansion into area codesof the other consumers in the cluster could be warranted.

In step 4914, the feature derivation module 4406 joins clusters toidentify edges (e.g., connecting lines between nodes). Once the nodesare constructed, the intersections (e.g., edges) may be computed “all atonce,” by computing, for each point, the set of node sets (not ref_tags,this time). That is, for each s in S, node_id_set(s) may be computed,which is an int[ ]. In some embodiments, if the cover is well behaved,then this operation is linear in the size of the set S, and we theniterate over each pair in node_id_set(s). There may be an edge betweentwo node_id's if they both belong to the same node_id_set( ) value, andthe number of points in the intersection is precisely the number ofdifferent node_id sets in which that pair is seen. This means that,except for the clustering step (which is often quadratic in the size ofthe sets S(d), but whose size may be controlled by the choice of cover),all of the other steps in the graph construction algorithm may be linearin the size of S, and may be computed quite efficiently.

It will be appreciated that it is possible, in some embodiments, to makesense in a fairly deep way of connections between various ref( ) mapsand/or choices of clustering. Further, in addition to computing edges(pairs of nodes), the embodiments described herein may be extended tocompute triples of nodes, etc. For example, the analysis module 320 maycompute simplicial complexes of any dimension (by a variety of rules) onnodes, and apply techniques from homology theory to the graphs to helpusers understand a structure in an automatic (or semi-automatic) way.

Further, it will be appreciated that uniform intervals in the coveringmay not always be a good choice. For example, if the points areexponentially distributed with respect to a given filter, uniformintervals can fail—in such case adaptive interval sizing may yielduniformly-sized S(d) sets, for instance.

In various embodiments, an interface may be used to encode techniquesfor incorporating third-party extensions to data access and displaytechniques. Further, an interface may be used to for third-partyextensions to underlying infrastructure to allow for new methods forgenerating coverings, and defining new reference spaces.

In this example, features that are within a node may be grouped togetherdue to their similarity. Further, nodes that are tightly groupedtogether (e.g., that produce flares of nodes isolated from other nodesbut interconnected with each other indicating that one or more nodesshare features as members) may also be grouped together. In thisdiscussion, a feature is considered to be a data point of the transposeof the analysis data. In one example, nodes that are tightly grouped(e.g., nearest neighbors) may be grouped together for identifying agroup of similar features (e.g., the similar features may include allthe members of the nodes that are grouped together).

In step 4510 of FIG. 45, the feature derivation module 4406 may furthergroup features identified in TDA analysis and/or from the outcomeanalysis engine 4426. In some embodiments, the feature derivation module4406 includes an autogrouping engine 4428 similar to autogroup module2002. For example, the autogrouping engine 4428 may include, forexample, a data structure module 2102, a partition generation module2104, a Q_subset score module 2106, a Q_max score module 2108, aQ_partition score module 2110, a partition selection module 2112, and/ora data control module 2114. One example of the feature derivation module4406 grouping features using autogrouping is discussed with regard toFIG. 50.

In step 4512, the modeler system 4202 (e.g., the feature selectionmodule 4408) determines correlations between one or more of the featureswith the identified output from the analysis data. As discussed herein,an output maybe identified within the analysis data. Correlation may bedetermined for each feature and the identified output (e.g., a subset ofthe analysis data associated with the output identifier received by themodeler system 4202). It will be appreciated that any correlation (e.g.,Pearsons, adjusted correlation, weighted correlation, reflectivecorrelation, scaled correlation, circular correlation, and/or the like)may be used to generate a correlation coefficient or other correlationidentifier.

In step 4514, the modeler system 4202 may determine a group correlationfor each group of features. In various embodiments, the featureselection module 4408 may determine a group correlation for each groupof features. In one example, the feature selection module 4408 maydetermine an average based on the individual correlations for eachfeature that is a member of the group. The group correlation may be theaverage. It will be appreciated that any statistical measure may be usedto determine the group correlation based on the individual correlationsof the members of the group of features.

In step 4516, the modeler system 4202 displays and/or generates a reportregarding the grouped features. In some embodiments, the featureselection module 4408 may order the feature groups based on the groupcorrelation of each group. In various embodiments, the interface module4220 displays or generates a report regarding the grouped features(e.g., the features being grouped through TDA of the analysis data andoptionally using automatic outcome analysis and/or autogroupingdescribed regarding FIGS. 50 and 51 herein).

FIG. 52 depicts a group feature interface 5200 for a PD model in someembodiments. The group feature interface 5200 may display the groups offeatures including the features that a are members of each group. Forexample, the group feature interface 5200 depicts the features 5202 thatare members of group 1 and the features 5204 that are members of group2. There may be any number of features of any number of groups. Thegroup feature interface 5200 may depict a correlation 5206 for eachfeature indicating correlation between that particular feature and theoutput from the analysis data. The group feature interface 5200 maydisplay the groups and/or features of each group in order ofcorrelation.

In various embodiments, the group feature interface 5200 may indicatethe number of possible models assuming all features indicated in thegroup feature interface 5200 are selected. In various embodiments, thefeature selection module 4408 may determine the number of possiblemodels based on the maximum number of features allowed for each model(e.g., four maximum features) and a number of selected features. In thisexample, the group feature interface 5200 indicates that, assuming allfeatures are selected, there are 2,342 possible models of the fourhundred total features.

The group feature interface 5200 may enable a user to select any numberof features for model generation. It will be appreciated that bygrouping similar features together, a reduced number of features of thegroup may be selected (e.g., one or two features from a group of sixfeatures) which may include the correlation of the features without theredundancy of adding additional features that have the same effect whichmay reduce the number of models to generate and review.

FIG. 53 depicts a group feature interface 5300 for a PD model where asubset of features are selected in some embodiments. In this example,the group feature interface 5300 may be the group feature interface 5200with any number of features that are selected. The features that areselected may be based on correlation, effect within the group offeatures of which the selected feature is a member, regulationrequirements, objective, business experience of the user, and/or anycombination of these.

In various embodiments, once features are selected in the group featureinterface 5300, the feature selection module 4408 may determine thenumber of possible models based on the maximum number of featuresallowed for each model (e.g., four maximum features) and the number ofselected features. In this example, the group feature interface 5200indicates that the total number of features is now 760 based on thenumber of features selected.

The group feature interface 5200 and/or 5300 may include an interactiveelement (e.g., button) that initiates creation of models based on theselected features.

In step 4518, the modeler system 4202 generates models for each selectedfeature combination. In one example, the model generation module 4412generates a model for each combination of selected features that includeone or more features up to the maximum number of features allowed (e.g.,up to the maximum number of features indicated by the businessadministrator, lead modeler, regulator, and/or the like).

For example, if there are a maximum of four features allowed and fourfeatures were selected, the models that may be generate may includemodels that include only one of the four selected features, models thatinclude each combination of two features of the selected four features,models that include each combination of three features of the selectedfour features, and a model including all four selected features.

In step 4520, the modeler system 4202 determines fit for each generatedmodel in view of the output of the analysis data (e.g., the outputoriginally identified). In some embodiments, the model fitting module4414 determines the fit for each model based on the identified output.

FIG. 54 depicts a regression graph for model fitting using one modelgenerated by the model generation module 4412. In this example, theR-square is 0.50, the vertical axis is the predicted and the horizontalaxis is actual. In various embodiments, the model fitting module 2014determines correlation and/or confidence of a model to fit a predictedmodel.

In various embodiments, the model fitting module 4414 determines thequality of the fit of the model using the selected features for thatparticular model to the identified output. In some embodiments, themodel fitting module 4414 determines an error estimation. Model fittingmay be performed in any number of ways.

In step 4522, the modeler system 4202 orders the generated models basedon the quality and confidence of model fitting. In various embodiments,the model recommendation module 4416 may order the models based onconfidence and quality of the fit to the output data.

FIG. 55 depicts a select model interface 5500 that may be generated bythe interface module 4220 in some embodiments. The select modelinterface 5500 may depict an ordering of the models based on quality offit. In various embodiments, the select model interface 5400 depictsmodels in order based on RSME, R-squares, A/C, Confidential interval,and/or the like (which may be generated by the model fitting module4414). The model recommendation module 4416 may recommend a model basedon RSME, R-squares, A/C, Confidential interval, and/or the like. Theselect model interface 5400 may also indicate the features for eachmodel. For example, model 1 may include 461+13.5*market volatility index(LAG2), model 2 may include 23+49.2*market volatility index(LAG2)+1.14*real disposable income on growth (SQ),+2.24*JY Futures. Theselect model interface 5500 may further include the graphs of selectedmodels in comparison with output.

In various embodiments, the select model interface 5500 may include aninteractive element to enable selection of one or more models (e.g., arecommended model and one or more candidate models) for review.

In various embodiments, the modeler system 4202 may track which modelsare selected as well as those in which review is completed. The modelersystem 4202 may track which models are deployed.

While the modeler system 4202 described herein describes generation ofmodels, it will be appreciated that the modeler system 4202 enablesusers to select different features and generate new models forcomparison against previously generated models or scenarios.

In step 4524, the modeler system 4202 may generate a scenario resultutilizing a selected model and a selection of a scenario associated withscenario data. For example, the scenario module 4418 may receive aselection of a model and a scenario selection (e.g., from the selectmodel interface 5400 or any interface). The scenario module 4418 mayutilize the selected model to create scenario results using the selectedscenario data. It will be appreciated that different regulators mayrequire testing of the model or exploring the result of the particularscenario using the model. In one example, if the model is trusted,different scenarios may be utilized to predict possible outcomes forinvestment decisions, satisfaction of regulations, stress testing,and/or the like.

In step 4526, the documentation module 4422 may generate a documentationreport regarding the recommended model and/or candidate models. Thedocumentation report may indicate the features of each model,correlation of each selected feature of each model (e.g., thecorrelation for the selected feature with the output of the analysisdata), group correlations of any groups, correlations of unselectedfeatures with the output of the analysis data, scenario output of anynumber of models, and/or the like.

In various embodiments, the documentation module 4422 logs informationregarding the source and contents of the analysis data, scenario data,output data and the like. The documentation module 4422 may log theeffect of any or all decisions and/or any and or all analysis. Forexample, the documentation module 4422 may log networks generated byTDA, metrics and/or lenses utilized, different possible groups offeatures, features of different groups, correlations for each group andgroup correlations, selected features, and generated models.

It will be appreciated that the documentation module 4422 logs theinformation and creates reports such that the model may be reviewed forcompliance with regulations and assessed for quality and effect.

FIG. 56 depicts an example model report 5600 in some embodiments. Themodel report 5600 indicates all or some of the different models created,features of different models, forecasts, model statistics, and/or modelnotes indicating model parameters, person responsible, modeler notes oflead modeler, reviewer notes, and/or the like.

FIG. 50 is a flowchart for outcome auto analysis to test different lensfunctions and/or different filter functions in some embodiments. FIG. 50may be similar to FIG. 31. In some embodiments, the analysis engine 4424may receive a lens function identifier, metric function identifier, andresolution identifier for use in TDA analysis. The outcome auto analysismay generate TDA networks using different lens functions and/ordifferent filter functions (and different combinations of lens andfilter functions) and then score the different networks to find the mostpreferred network relative to the other generated networks.

In some embodiments, the outcome analysis engine 4426 may include orcomprise an outcome analysis module 2902 depicted in FIG. 30. In someembodiments, the outcome analysis engine 4426 may include ametric-selection module 3002, a metric-lens combination selection module3004, a lens parameter selection module 3006, a group identificationmodule 3008, a group score module 3010, a graph score module 3012, agraph selection module 3014, and/or an outcome visualization module3016. In step 5002, the feature derivation module 4406 may receive(e.g., transposed) analysis data including or from a large data set. Invarious embodiments, the data received in step 5002 may include one ormore shared characteristics in columns related to each row or datapoint. In one example, the one or more shared characteristics mayinclude one or more outcomes (e.g., identified by or for the modeldefinition module 4402). The outcomes may, in some embodiments, becategorical. If the data is medical data, the shared characteristics mayinclude, for example, patient outcomes after treatment(s), services, orthe like. Shared characteristics, such as outcomes (e.g., the output),may be continuous or discrete. A discrete outcome may individuallyseparate and distinct (e.g., the data may be divided in part on thosewho survived and those who died). Alternately, the outcomes may bemeasured as part of a continuum. In some embodiments, the metricselection module 3002 may perform discretization of continuous featuresof outcomes from one or more outcome columns of the data to generateoutcome categories that are discrete.

In various embodiments, the method described herein may search throughexisting metrics and lenses to produce one or more graphs that separatethe outcome groups. The separations may not necessarily be based ongeometry. One existing example method includes steps for choosing orselecting one or more metrics, choose or select one or more lenses, andchoose or select one or more scale parameters. Those metrics that arechosen or selected may make the outcome (e.g., shared characteristic(s))column(s) “continuous” with respect to the metric. Those lenses (e.g.,metric-lens(es) combination(s)) that are chosen or selected may assistin localizing outcome values. Those scale parameters that are chosen orselected may both localize and separate different outcome values withoutmaking too many singeltons (e.g., nodes of a graph of visualization witha single data point as a member or few data points as a member). Theseprocesses are further described below.

The data may be related to any science, profession, service, or the like(e.g. the data may be medical data, financial data, scientific data,energy data, or the like).

In step 5004, the feature derivation module 4406 selects a subset ofmetrics. The subset may include any number of metrics from a set ofmetrics or pseudo-metrics (e.g., does not satisfy the triangleinequality) from a set of metrics. Metrics that are selected may be usedwith received data to generate “continuous” results with respect to theshared characteristics (e.g., outcome column(s)). For example, pointsthat are similar in the metric may have similar or the same outcomecharacteristics (e.g., outcome categories).

In various embodiments, the feature derivation module 4406 buildsneighborhoods for each point. For each point, the feature derivationmodule 4406 may count the number of points whose nearest neighbor hasthe same shared characteristic category as the particular point togenerate a metric score for each metric. Different metric scores may becompared to select those metrics that are “continuous” with respect tothe shared characteristics. The process is further described with regardto FIG. 32 with the feature derivation module 4406 performing functionsdiscussed therein.

In step 5006, the feature derivation module 4406 selects a subset ofmetric-lens(es) combinations based in part on the subset of metrics. Invarious embodiments, metric-lens(es) combinations may be selected totest (e.g., any number of metric-lens(es) combinations may be tested).The metric-lens combination selection module 3002 may select themetric-lens(es) combinations that localize outcome values (e.g.,relative to other metric-lens(es) combinations. The process is furtherdescribed with regard to FIG. 35 with the feature derivation module 4406performing functions discussed therein.

In step 5008, the feature derivation module 4406 may select aresolution. In various embodiments, the feature derivation module 4406chooses resolution and gain. The choice of resolution may depend on thenumber of points in the space and the number of lenses. The process isfurther described with regard to FIG. 38 with the feature derivationmodule 4406 performing functions discussed therein.

In step 5010, the feature derivation module 4406 generates a topologicalgraph using at least some of the metric-lens(es) combinations from thesubset of metric-lens combinations and at least one of the resolutionsfrom subset of resolutions. The process is further described with regardto FIG. 38 with the feature derivation module 4406 performing functionsdiscussed therein.

In step 5012, the feature derivation module 4406 characterizes nodes ofeach group based on outcome(s) (e.g., shared characteristic(s)). Invarious embodiments, the feature derivation module 4406 colors orotherwise denotes each node of the topological graph based on outcome(s)of member points of that node or distribution of outcome(s) of memberpoints of that node. The process is further described with regard toFIG. 38 with the feature derivation module 4406 performing functionsdiscussed therein.

In step 5014, the feature derivation module 4406 identify groups ofnodes that share the same or similar outcome of each graph. This processmay utilize autogrouping described herein to determine edge weightbetween nodes, identify a partition that includes groupings of nodesbased on distribution of outcome(s) of data points, or both. The processis further described with regard to FIG. 38 with the feature derivationmodule 4406 performing functions discussed therein.

In step 5016, the feature derivation module 4406 scores each graph basedon identified groups. In various embodiments, the feature derivationmodule 4406 may score each group in a graph based on entropy of groups,number of data points, number of nodes in a group, number of outcomecategories (e.g., discrete outcome groups of the outcomes identifiedusing the originally received data), or any combination. The graph scoremodule 3012 may generate a graph score for each graph based on the groupscores of that graph. These processes are further described with regardto FIG. 38 with the feature derivation module 4406 performing functionsdiscussed therein.

In step 5018, the feature derivation module 4406 may select one or moregraphs based on the graph scores. The feature derivation module 4406 mayselect a graph in any number of ways. For example, the featurederivation module 4406 may select one or more graphs based on thehighest scores, may select the top graph based on score, may select allgraphs with graph scores above a predetermined threshold, may select apredetermined number of graphs in order of the highest score, or may notselect any graph below a predetermined threshold. The process is furtherdescribed with regard to FIG. 38 with the feature derivation module 4406performing functions discussed therein.

As a result, a preferred network may be selected and then features ofnodes and features that are members of groupings of nodes may beidentified as being similar.

In some embodiments, the feature derivation module 4406 may rank ororder the graphs based on the graph scores. In some embodiments, foreach graph, the feature derivation module 4406 displays informationrelated to generating the particular graph, including for example metricinformation indicating the metric used, metric-lens informationindicating the lens(es) used, resolution information indicating theresolution and gain used, graph score, or any combination. The processis further described with regard to FIG. 38 with the feature derivationmodule 4406 performing functions discussed therein.

FIG. 51 is an example flowchart for autogrouping in some embodiments.FIG. 51 may be similar to FIG. 22. In this example, groups of nodes maybe grouped using autogrouping discussed herein to identify nodescontaining similar features. As discussed herein, features may besimilar if they have a similar effect (e.g., distance and/orcorrelation) relative to the output data (e.g., identified by the modeldefinition module 4402).

In this example, the feature derivation module 4406 receives a set S={A,B, C, D, E, F, G} and performs autogrouping to identify a selectedpartition of a forest based on S. Elements of set S may be, for example,nodes of a graph, the nodes including any number of features. The graphmay be a topological data analysis graph of nodes and edges as describedherein. In some embodiments, the graph may be any network graph thatincludes nodes, neighborhoods, groupings, communities, and/or datapoints.

The embodiment of the Q_Partition in this example is simply the sum overthe subsets of the partition P of the Q_Subset scores on each subset.For example, if P={{A, B, C}, {D}, {E, F}, {G}}, thenQ_Partition(P)=Q_Subset({A, B, C})+Q_Subset({D})+Q_Subset({E,F})+Q_Subset({G}).

In step 5102, the feature derivation module 4406 receives the set S andgenerates an initial partition which are the singletons of the set S={A,B, C, D, E, F, G}, namely, P_0={{A}, {B}, {C}, {D}, {E}, {F}, {G} }.This is illustrated in FIG. 23 as the bottom row (2302) of the depictedforest.

In step 5104, the feature derivation module 4406 computes the Q_Subsetscore on each subset of the partition P_0. The scoring function in thisexample, may be a modularity scoring function discussed herein.

In step 5106, the feature derivation module 4406 computes the maximalpartition of each subset a of P_0 from the children of the subset a inthe constructed forest. Since the subsets a in P_0 have no children inthe forest, the maximal partition of the children of the subset a isitself. Namely, for each subset a in P_0, MaximalPartitionChildren(a)=a.

In step 5108, the feature derivation module 4406 computes Q_Max on eachsubset of P_0. Recall that since the subsets in P_0 do not have anychildren, for each subset a in P_0,

$\begin{matrix}{{{Q\_ Max}(a)} = {\max\left( {{{Q\_ Subset}(a)},{{Q\_ Partition}\left( {{MaximalPartitionChildren}(a)} \right)}} \right.}} \\{= {\max\left( {{{Q\_ Subset}(a)},{{Q\_ Partition}(a)}} \right)}} \\{= {{\max\left( {{{Q\_ Subset}(a)},{{Q\_ Subset}(a)}} \right)} = {{Q\_ Subset}(a)}}} \\{= 0.5}\end{matrix}$

In step 5110, the feature derivation module 4406 optionally records themaximal partition of each subset a in P_0 to be partition of the subseta that generated the Q_Max for that subset. Thus the feature derivationmodule 4406 records the MaximalPartition(a)=a in this initial partition.

In step 5112, the feature derivation module 4406 computes the nextpartition P_1 (the row labeled 2304 in FIG. 23). In this example, thefeature derivation module 4406 groups subsets {A} and {B} into thesubset {A, B} and subsets {D} and {E} into subset {D, E}. The datafeature derivation module 4406 preserved the subsets {C}, {F}, and {G}from the partition P_0 in the partition P_1.

It will be appreciated that the next partition P_1 may group subsets ofprevious partition(s) (e.g., partition 2304) in any number of ways. Forexample, the feature derivation module 4406 may group a predeterminednumber of subsets together at random and/or may group two or moresubsets together based on the elements (e.g., based on the underlyingdata that the elements represent). In one example, the featurederivation module 4406 may group elements together using a distancemetric and/or any other functions to define relationships in theunderlying data and/or within a similarity space (e.g., referencespace).

In various embodiments, the feature derivation module 4406 may determinewhether the system ends and/or whether a new partition is to becomputed. It will be appreciated that the feature derivation module 4406may perform the determination based on any number of ways. In someembodiments, the feature derivation module 4406 determines if the nextgenerated partition is equal to the previous partition. If the twopartitions are equal (e.g., have the same subsets), the method mayterminate, otherwise the method may continue to step 5114.

In some embodiments, the feature derivation module 4406 terminates themethod after a predetermined number of partitions are generated, if apredetermined number of roots are found, and/or the like. In variousembodiments, the feature derivation module 4406 may terminate the methodif a predetermined number of subsets are present in a computedpartition. In another example, the feature derivation module 4406terminate the method after a predetermined period of time, apredetermined period of memory usage, or based on any threshold (e.g.,the threshold being calculated based on the amount of data received).

In step 5114, the feature derivation module 4406 computes the Q_Subsetscore on each subset of the partition P_1. In the example of FIG. 23,the Q_subset score module 2106 computes Q_Subset({A, B})=0.5 andQ_Subset({D,E})=2. In one example, the Q_subset score module 2106calculates a modularity score for elements A and B for Subset {A,B} anda modularity score for elements D and E for Subset {D,E}. As discussedherein, the modularity score may be based on the edges of nodes A and Bfor Q_Subset({A, B}) modularity score and based on the edges of nodes Dand E for Q_Subset({D, E}) modularity score.

As was discussed in the paragraph above describing step 5104, Q_Subsetof each singleton subset is 0.5 (e.g., the previous Q_Subset score forsingleton subsets in step 2304 remains unchanged from step 2302). Thesescores are associated with each subset and are visualized in the FIG. 23as Q_Sub in 2304.

In step 5116, the feature derivation module 4406 then computes themaximal partition at the children of each subset of P_1. The maximalpartition of the children of the subsets {C}, {F}, and {G} are again theoriginal singleton subset. The maximal partition of the children {A, B}is the set including the maximal partitions of the children of {A, B},namely {{A}, {B} } as depicted in partition 2304 in FIG. 23. Similarlythe maximal partition of the children of {D, E} is the set {{D}, {E} }as also depicted in partition 2304 in FIG. 23.

In step 5118, the feature derivation module 4406 computes the Q_Max oneach subset of P_1. Recall Q_Max(a)=max(Q_Subset(a),Q_Partition(MaximalPartitionChildren(a)). For the subset {A, B}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {A,B} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {A,B} \right\} \right)},{{Q\_ Partition}\left( \left\{ {\left\{ A \right\},\left\{ B \right\}} \right\} \right)}} \right)}} \\{= {\max\left( {{.5},{{{Q\_ Subset}\left( \left\{ A \right\} \right)} + {{Q\_ Subset}\left( \left\{ B \right\} \right)}}} \right.}} \\{= {\max\left( {0.5,1} \right)}} \\{= 1}\end{matrix}$

For the subset {D, E}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {D,E} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {D,E} \right\} \right)},{{Q\_ Partition}\left( \left\{ {\left\{ D \right\},\left\{ E \right\}} \right\} \right)}} \right)}} \\{= {\max\left( {2,{{{Q\_ Subset}\left( \left\{ D \right\} \right)} + {{Q\_ Subset}\left( \left\{ E \right\} \right)}}} \right.}} \\{= {\max\left( {2,1} \right)}} \\{= 2.}\end{matrix}$

As displayed in partition 2304 of FIG. 23, Q_Max of {A,B} is 1 and Q_Maxof {D,E} is 2. The Q_Max of singletons {C}, {F}, and {G} in partition2304 remain consistent with the respective subsets in partition 2302.Namely, the Q_Max of each of {C}, {F}, and {G} is 0.5.

In step 5120, the feature derivation module 4406 optionally records themaximal partition of each subset a in P_1 that resulted in the Q_Maxscore.

Step 5112 is repeated. The feature derivation module 4406 computes thenext partition P_2, depicted in FIG. 23 as row (partition) 2306. Invarious embodiments, the feature derivation module 4406 may determinewhether the system ends and/or whether a new partition is to becomputed. It will be appreciated that the feature derivation module 4406may perform the determination based on any number of ways.

In step 5114, the feature derivation module 4406 computes the Q_Subsetscore on each subset of the partition P_2. In this example, the Q_subsetscore module 2106 computes Q_Subset({A, B, C})=2 and Q_Subset({D, E,F})=1.5. Again, Q_Subset({G})=0.5. These scores are recorded with eachsubset and are visualized in the FIG. 23 in partition 2306.

In step 5116, the feature derivation module 4406 computes the maximalpartition at the children of each subset of P_2. The maximal partitionof the children{G} is the subset {G}. The maximal partition of thechildren {A, B, C} is the set consisting of the maximal partitions ofthe children of {A, B, C}, namely {MaxPartition({A,B}),MaxPartition({C})={{A}, {B}, {C} }. Similarly the maximal partition ofthe children of {D, E, F} is the set {MaxPartition({D, E}),MaxPartition({F})}={{D, E}, {F}}.

In step 5118, the feature derivation module 4406 computes the Q_Max oneach subset of P_2. Recall Q_Max(a)=max(Q_Subset(a),Q_Partition(MaximalPartitionChildren(a)). For the subset {A, B, C}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {A,B,C} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {A,B,C} \right\} \right)},} \right.}} \\{{Q\_ Partition}\left( \left\{ {\left\{ A \right\},\left\{ B \right\},\left\{ C \right\}} \right) \right)} \\{= {\max\left( {2,{{{Q\_ Subset}\left( \left\{ A \right\} \right)} + {{Q\_ Subset}\left( \left\{ B \right\} \right)} +}} \right.}} \\\left. {{Q\_ Subset}\left( \left\{ C \right\} \right)} \right) \\{= {\max\left( {2,1.5} \right)}} \\{= 2}\end{matrix}$

For the subset {D, E, F}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {D,E,F} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {D,E,F} \right\} \right)},} \right.}} \\\left. {{Q\_ Partition}\left( \left\{ {\left\{ {D,E} \right\},\left\{ F \right\}} \right\} \right)} \right) \\{= {\max\left( {1.5,{{{Q\_ Subset}\left( \left\{ {D,E} \right\} \right)} + {{Q\_ Subset}\left( \left\{ F \right\} \right)}}} \right.}} \\{= {\max\left( {1.5,2.5} \right)}} \\{= 2.5}\end{matrix}$

As displayed in partition 2306 of FIG. 23, Q_Max of {A,B,C} is 2 andQ_Max of {D,E,F} is 2.5 The Q_Max of singleton{G} in partition 2306remains consistent with the respective subset in partition 2304. Namely,the Q_Max {G} is 0.5.

In step 5120, the feature derivation module 4406 optionally records themaximal partition of each subset a in P_2 that resulted in the Q_Maxscore. As seen above, MaxPartition({A, B, C})={{A, B, C}} andMaxPartition({D, E, F})={{D, E}, {F}}.

Step 5112 is repeated. The feature derivation module 4406 computes thenext partition P_3, depicted in FIG. 23 as row (partition) 2308. Thefeature derivation module 4406 may determine whether the system endsand/or whether a new partition is to be computed.

In step 5114, the feature derivation module 4406 computes the Q_Subsetscore on each subset of the partition P_3. In this example, the featurederivation module 4406 computes Q_Subset({A, B, C})=2 and Q_Subset({D,E, F, G})=1. These scores are recorded with each subset and arevisualized in FIG. 23 in partition 2308.

In step 5116, the feature derivation module 4406 computes the maximalpartition at the children of each subset of P_3. The maximal partitionof the children {A, B, C} is the set consisting of the maximalpartitions of the children of {A, B, C}, namely {MaxPartition({A,B,C})}={{A, B, C}. Similarly the maximal partition of the children of {D,E, F, G} is the set {MaxPartition({D, E, F}), MaxPartition({G})}={{D,E}, {F}, {G}}.

This is shown in FIG. 23 for each subset of partition 2308 asMaxP={A,B,C} for subset {A,B,C} and MaxP={{D,E},{F},{G}} for subset{D,E,F,G}.

In step 5118, the feature derivation module 4406 computes the Q_Max oneach subset of P_3. Recall Q_Max(a)=max(Q_Subset(a),Q_Partition(MaximalPartitionChildren(a)). For the subset {A, B, C}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {A,B,C} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {A,B,C} \right\} \right)},} \right.}} \\\left. {{Q\_ Partition}\left( \left\{ {A,B,C} \right\} \right)} \right) \\{= {\max\left( {2,{{Q\_ Subset}\left( \left\{ {A,B,C} \right\} \right)}} \right)}} \\{= 2}\end{matrix}$

For the subset {D, E, F, G}:

$\begin{matrix}{{{Q\_ Max}\left( \left\{ {D,E,F,G} \right\} \right)} = {\max\left( {{{Q\_ Subset}\left( \left\{ {D,E,F,G} \right\} \right)},} \right.}} \\\left. {{Q\_ Partition}\left( \left\{ {\left\{ {D,E} \right\},\left\{ F \right\},\left\{ G \right\}} \right\} \right)} \right) \\{= {\max\left( {1,{{{Q\_ Subset}\left( \left\{ {D,E} \right\} \right)} + {{Q\_ Subset}\left( {\left\{ F \right\} +} \right.}}} \right.}} \\{{Q\_ Subset}\left( \left\{ G \right\} \right)} \\{= {\max\left( {1,3} \right)}} \\{= 3}\end{matrix}$

As displayed in partition 2308 of FIG. 23, Q_Max of {A,B,C} is 2 andQ_Max of {D,E,F,G} is 3.

In step 5120, the feature derivation module 4406 optionally records themaximal partition of each subset a in P_3 that resulted in the Q_Maxscore. As seen above, MaxPartition({A, B, C})={{A, B, C}} andMaxPartition({D, E, F, G})={{D, E}, {F}, {G}}.

Although not depicted in method 5100, the method may continue. Forexample, the feature derivation module 4406 may identify and/or generatea preferred partition from that maximizes one or more scoring functions.In this example, the preferred partition is the MaxPartition. Asdiscussed immediately above, the maximal partition of each subset in P_3is MaxPartition({A, B, C})={{A, B, C}} and MaxPartition({D, E, F,G})={{D, E}, {F}, {G}}. The feature derivation module 4406 may identifyand/or generate the autogrouped partition {{A, B, C}, {{D, E}, {F}, {G}.

The feature derivation module 4406 may provide the identified and/orgenerated autogrouped partition in a report and/or identify theautogrouped partition in data or a graph. In this example, theautogrouped partition may include, for example, groups of features thathave similar functions as discussed regarding TDA, outcome analysis,and/or autogrouping herein.

It will be appreciated that the functions of the modeler system 4202 maybe performed by any number of modules. Further, it will be appreciatedthat one or more functions of the modeler system 4202 may be performedby other systems (e.g., TDA may be performed by an analysis serversimilar to that described with regard to FIG. 2).

The above-described functions and components can be comprised ofinstructions that are stored on a storage medium (e.g., a computerreadable storage medium). The instructions can be retrieved and executedby a processor. Some examples of instructions are software, programcode, and firmware. Some examples of storage medium are memory devices,tape, disks, integrated circuits, and servers. The instructions areoperational when executed by the processor (e.g., a data processingdevice) to direct the processor to operate in accord with embodiments ofthe present invention. Those skilled in the art are familiar withinstructions, processor(s), and storage medium.

The present invention has been described above with reference toexemplary embodiments. It will be apparent to those skilled in the artthat various modifications may be made and other embodiments can be usedwithout departing from the broader scope of the invention. Therefore,these and other variations upon the exemplary embodiments are intendedto be covered by the present invention.

What is claimed is:
 1. A non-transitory computer readable mediumincluding executable instructions, the instructions being executable bya processor to perform a method, the method comprising: receivinganalysis data and output indicator, the output indicator indicating asubset of data of the analysis data, the analysis data includingmultiple dimensions associated with data points; receiving a lensfunction identifier, a metric function identifier, and a resolutionfunction identifier; mapping data points from a transposition of theanalysis data, to a reference space utilizing a lens function identifiedby the lens function identifier, the transposition of the analysis datatransforming the analysis data such that the features are data points,the mapping of data points being performed by applying the lensfunctions across dimensions for each data point of the transposition ofthe analysis data; generating a cover of the reference space using aresolution function identified by the resolution identifier; clusteringthe data points mapped to the reference space using the cover and ametric function identified by the metric function identifier todetermine each node of a plurality of nodes of a graph, each nodeincluding at least one data point; for each node, identifying datapoints that are members of that node to identify similar features;grouping features that are members of the same node as being similar toeach other; for each feature, determining correlation with at least someof the subset of data of the analysis data and generate a correlationscore; displaying at least a subset of groups that include features thatare similar to each other and display the correlation score for eachdisplayed feature; receiving a selection of a subset of features fromthe at least the subset of groups; generating a set of models, eachmodel including at least one of the selection of the subset of features;determining fit of each generated model to the subset of data of theanalysis data and generate a model score; and generating a reportrecommending the model with the highest score.
 2. The non-transitorycomputer readable medium of claim 1, the method further comprisingreceiving a maximum number of features and wherein generating a set ofmodels comprising generating a model for every possible combination ofthe selection of the subset of features.
 3. The non-transitory computerreadable medium of claim 1, the method further comprising receivingmodel parameters and wherein every model that is generated is based onthe model parameters.
 4. The non-transitory computer readable medium ofclaim 1, the method further comprising receiving scenario data andapplying a selected model from the set of models to the scenario data togenerate scenario results.
 5. The non-transitory computer readablemedium of claim 1, the method further comprising documenting thefeatures of the analysis data, the subset of the groups that includefeatures that are similar to each other, the correlation score for eachfeature, the selection of the subset of the features, the model with thehighest score, and the highest score.
 6. The non-transitory computerreadable medium of claim 1, the documenting further comprisingindicating the features that were not selected and correlation scores ofeach of the features that were not selected.
 7. The non-transitorycomputer readable medium of claim 1, the method further comprisinggenerating a group score for each of the subset of groups, the groupscore being based on the correlation score of each of the features thatare members of the group, and ordering each of the subset of the groupsbased on the group score.
 8. The non-transitory computer readable mediumof claim 1, wherein the metric is one of a set of metrics, and, themethod further comprises for each for each metric of a set of metrics:for each point in the analysis data, determining a point in the data setclosest to that particular data point using that particular metric andchange a metric score if that particular data point and the point in thedata set closest to that particular data point share a same or similarshared characteristic; comparing metric scores associated with differentmetrics of the set of metrics; selecting one or more metrics from theset of metrics based at least in part on the metric score to generate asubset of metrics; for each metric of the subset of metrics, evaluatingat least one metric-lens combination by calculating a metric-lens scorebased on entropy of shared characteristics across subspaces of areference map generated by the metric-lens combination; selecting one ormore metric-lens combinations based at least in part on the metric-lensscore to generate a subset of metric-lens combinations; generatingtopological representations using the received data set, eachtopological representation being generated using at least onemetric-lens combination of the subset of metric-lens combinations, eachtopological representation including a plurality of nodes, each of thenodes having one or more data points from the data set as members, atleast two nodes of the plurality of nodes being connected by an edge ifthe at least two nodes share at least one data point from the data setas members; scoring each group within each topological representationbased, at least in part, on entropy, to generate a group score for eachgroup; and scoring each topological representation based on the groupscores of each group of that particular topological representation togenerate a graph score for each topological representation, wherein thesubset of groups that include features that are similar to each other isselected from nodes of the topological representation with the highestscore.
 9. The non-transitory computer readable medium of claim 1,further comprising building a first partition of subsets of the analysisdata, each subset of the first partition containing elements beingexclusive of other subsets of the first partition; computing a firstsubset score for each subset of the first partition using a scoringfunction based on the subset of the data of the analysis data;generating a next partition including all of the elements of the firstpartition, the next partition including at least one subset thatincludes the elements of two or more subsets of the first partition,each particular subset of the next partition being related to one ormore subsets of a previously generated partition if that particularsubset shares membership of at least one element with the one or moresubsets of the previously generated partition; computing a second subsetscore for each subset of the next partition using the scoring function;defining a max score for each particular subset of the next partitionusing a max score function, each max score being based on maximal subsetscores of that particular subset of the next partition and at least thesubsets of the first partition related to that particular subset;selecting output subsets from all subsets of the next partition and thepreviously generated partitions including the first partition, theoutput subsets together including all elements of the first partition,selection of each of the output subsets being made, at least in part,using a maximum score of previously computed subset scores, the maximumscore being a largest score of all subset scores of the next partitionand previously generated partitions including the first partition; andwherein the subset of groups that include features that are similar toeach other is selected from nodes of an output partition containing theoutput subsets, the output subsets of the output partition beingassociated with the received analysis data, each subset of the outputpartition containing elements being exclusive of other subsets of theoutput partition.
 10. A method comprising: receiving analysis data andoutput indicator, the output indicator indicating a subset of data ofthe analysis data, the analysis data including multiple dimensionsassociated with data points; receiving a lens function identifier, ametric function identifier, and a resolution function identifier;mapping data points from a transposition of the analysis data, to areference space utilizing a lens function identified by the lensfunction identifier, the transposition of the analysis data transformingthe analysis data such that the features are data points, the mapping ofdata points being performed by applying the lens functions acrossdimensions for each data point of the transposition of the analysisdata; generating a cover of the reference space using a resolutionfunction identified by the resolution identifier; clustering the datapoints mapped to the reference space using the cover and a metricfunction identified by the metric function identifier to determine eachnode of a plurality of nodes of a graph, each node including at leastone data point; for each node, identifying data points that are membersof that node to identify similar features; grouping features that aremembers of the same node as being similar to each other; for eachfeature, determining correlation with at least some of the subset ofdata of the analysis data and generate a correlation score; displayingat least a subset of groups that include features that are similar toeach other and display the correlation score for each displayed feature;receiving a selection of a subset of features from the at least thesubset of groups; generating a set of models, each model including atleast one of the selection of the subset of features; determining fit ofeach generated model to the subset of data of the analysis data andgenerate a model score; and generating a report recommending the modelwith the highest score.
 11. The method of claim 10, the method furthercomprising receiving a maximum number of features and wherein generatinga set of models comprising generating a model for every possiblecombination of the selection of the subset of features.
 12. The methodof claim 10, the method further comprising receiving model parametersand wherein every model that is generated is based on the modelparameters.
 13. The method of claim 10, the method further comprisingreceiving scenario data and applying a selected model from the set ofmodels to the scenario data to generate scenario results.
 14. The methodof claim 10, the method further comprising documenting the features ofthe analysis data, the subset of the groups that include features thatare similar to each other, the correlation score for each feature, theselection of the subset of the features, the model with the highestscore, and the highest score.
 15. The method of claim 10, thedocumenting further comprising indicating the features that were notselected and correlation scores of each of the features that were notselected.
 16. The method of claim 10, the method further comprisinggenerating a group score for each of the subset of groups, the groupscore being based on the correlation score of each of the features thatare members of the group, and ordering each of the subset of the groupsbased on the group score.
 17. The method of claim 10, wherein the metricis one of a set of metrics, and, the method further comprises for eachfor each metric of a set of metrics: for each point in the analysisdata, determining a point in the data set closest to that particulardata point using that particular metric and change a metric score ifthat particular data point and the point in the data set closest to thatparticular data point share a same or similar shared characteristic;comparing metric scores associated with different metrics of the set ofmetrics; selecting one or more metrics from the set of metrics based atleast in part on the metric score to generate a subset of metrics; foreach metric of the subset of metrics, evaluating at least onemetric-lens combination by calculating a metric-lens score based onentropy of shared characteristics across subspaces of a reference mapgenerated by the metric-lens combination; selecting one or moremetric-lens combinations based at least in part on the metric-lens scoreto generate a subset of metric-lens combinations; generating topologicalrepresentations using the received data set, each topologicalrepresentation being generated using at least one metric-lenscombination of the subset of metric-lens combinations, each topologicalrepresentation including a plurality of nodes, each of the nodes havingone or more data points from the data set as members, at least two nodesof the plurality of nodes being connected by an edge if the at least twonodes share at least one data point from the data set as members;scoring each group within each topological representation based, atleast in part, on entropy, to generate a group score for each group; andscoring each topological representation based on the group scores ofeach group of that particular topological representation to generate agraph score for each topological representation, wherein the subset ofgroups that include features that are similar to each other is selectedfrom nodes of the topological representation with the highest score. 18.The method of claim 10, further comprising building a first partition ofsubsets of the analysis data, each subset of the first partitioncontaining elements being exclusive of other subsets of the firstpartition; computing a first subset score for each subset of the firstpartition using a scoring function based on the subset of the data ofthe analysis data; generating a next partition including all of theelements of the first partition, the next partition including at leastone subset that includes the elements of two or more subsets of thefirst partition, each particular subset of the next partition beingrelated to one or more subsets of a previously generated partition ifthat particular subset shares membership of at least one element withthe one or more subsets of the previously generated partition; computinga second subset score for each subset of the next partition using thescoring function; defining a max score for each particular subset of thenext partition using a max score function, each max score being based onmaximal subset scores of that particular subset of the next partitionand at least the subsets of the first partition related to thatparticular subset; selecting output subsets from all subsets of the nextpartition and the previously generated partitions including the firstpartition, the output subsets together including all elements of thefirst partition, selection of each of the output subsets being made, atleast in part, using a maximum score of previously computed subsetscores, the maximum score being a largest score of all subset scores ofthe next partition and previously generated partitions including thefirst partition; and wherein the subset of groups that include featuresthat are similar to each other is selected from nodes of an outputpartition containing the output subsets, the output subsets of theoutput partition being associated with the received analysis data, eachsubset of the output partition containing elements being exclusive ofother subsets of the output partition.
 19. A system comprising: aprocessor; a memory including instructions to configure the processorto: receive analysis data and output indicator, the output indicatorindicating a subset of data of the analysis data, the analysis dataincluding multiple dimensions associated with data points; receive alens function identifier, a metric function identifier, and a resolutionfunction identifier; map data points from a transposition of theanalysis data, to a reference space utilizing a lens function identifiedby the lens function identifier, the transposition of the analysis datatransforming the analysis data such that the features are data points,the mapping of data points being performed by applying the lensfunctions across dimensions for each data point of the transposition ofthe analysis data; generate a cover of the reference space using aresolution function identified by the resolution identifier; cluster thedata points mapped to the reference space using the cover and a metricfunction identified by the metric function identifier to determine eachnode of a plurality of nodes of a graph, each node including at leastone data point; for each node, identify data points that are members ofthat node to identify similar features; group features that are membersof the same node as being similar to each other; for each feature,determine correlation with at least some of the subset of data of theanalysis data and generate a correlation score; display at least asubset of groups that include features that are similar to each otherand display the correlation score for each displayed feature; receive aselection of a subset of features from the at least the subset ofgroups; generate a set of models, each model including at least one ofthe selection of the subset of features; determine fit of each generatedmodel to the subset of data of the analysis data and generate a modelscore; and generate a report recommending the model with the highestscore.